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In order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision‐making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper. [ABSTRACT FROM AUTHOR]

There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence‐based predictions and ensure transparency in decision‐making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision‐making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non‐visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an "attention guided Grad‐CAM" that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community. [ABSTRACT FROM AUTHOR]

In recent years, with the development of low‐earth orbit broadband satellites, the combination of multi‐path transmission and software‐defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi‐path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi‐path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real‐time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi‐objective optimization‐based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high‐speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN‐MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN‐MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS. [ABSTRACT FROM AUTHOR]

Power line inspection is an important means to eliminate hidden dangers of power lines. It is a difficult research problem how to solve the low accuracy of power line inspection based on deep neural network (DNN) due to the problems of multi‐view‐shape, small‐size object. In this paper, an automatic detection model based on Feature visual clustering network (FVCNet) for power line inspection is established. First, an unsupervised clustering method for power line inspection is proposed, and applied to construct a detection model which can recognize multi‐view‐shape objects and enhanced object features. Then, the bilinear interpolation method is used to Feature enhancement method, and the enhanced high‐level semantics and low‐level semantics are fused to solve the problems of small object size and single sample. In this paper, FVCNet is applied to the MS‐COCO 2017 data set and self‐made power line inspection data set, and the test accuracy is increased to 61.2% and 82.0%, respectively. Compared with other models, especially for the categories that are greatly affected by multi‐view‐shape, the test accuracy has been improved significantly. [ABSTRACT FROM AUTHOR]

The analysis of sentiments and mining of opinions have become more and more important in years because of the development of social media technologies. The methods that utilize natural language processing and lexicon‐based sentiment analysis techniques to analyze people's opinions in texts require the proper extraction of sentiment words to ensure accuracy. The current issue is tackled with a novel perspective in this paper by introducing a hybrid sentiment analysis technique. This technique brings together Convolutional Neural Network (CNN) and Hidden Markov Models (HMMs), to accurately categorize text data and pinpoint feelings. The proposed method involves 1D convolutional‐layer CNN to extract hidden features from comments and applying HMMs on a feature‐sentence matrix, allowing for the utilization of word sequences in extracting opinions. The method effectively captures diverse text patterns by extracting a range of features from texts using CNN. Text patterns are learned using text HMM by calculating the probabilities between sequences of feature vectors and clustering feature vectors. The paper's experimental evaluation employs benchmark datasets such as CR, MR, Subj, and SST2, demonstrating that the proposed method surpasses existing sentiment analysis techniques and traditional HMMs. One of its strengths is to analyze a range of text patterns and identify crucial features that recognize the emotion of different pieces of a sentence. Additionally, the research findings highlight the improved performance of sentiment analysis tasks through the strategic use of zero padding in conjunction with the masking technique. [ABSTRACT FROM AUTHOR]

Modelling one‐to‐many type mappings in problems with a temporal component can be challenging. Backpropagation is not applicable to networks that perform discrete sampling and is also susceptible to gradient instabilities, especially when applied to longer sequences. In this paper, we propose two recurrent neural network architectures that leverage stochastic units and mixture models, and are trained with target propagation. We demonstrate that these networks can model complex conditional probability distributions, outperform backpropagation‐trained alternatives, and do not rapidly degrade with increased time horizons. Our main contributions consist of the design and evaluation of the architectures that enable the networks to solve multi‐model problems with a temporal dimension. This also includes the extension of the target propagation through time algorithm to handle stochastic neurons. The use of target propagation provides an additional computational advantage, which enables the network to handle time horizons that are substantially longer compared to networks fitted using backpropagation. [ABSTRACT FROM AUTHOR]

Deep learning models have overcome traditional machine learning techniques for text classification domains in the field of natural language processing (NLP). Since, NLP is a branch of machine learning, used for interpreting language, classifying text of interest, and the same can be applied to analyse the medical clinical electronic health records. Medical text consists of lot of rich data which can altogether provide a good insight, by determining patterns from the clinical text data. In this paper, bidirectional‐long short‐term memory (Bi‐LSTM), bi‐LSTM attention and bidirectional encoder representations from transformers (BERT) base models are used to classify the text which are of privacy concern to a person and which should be extracted and can be tagged as sensitive. This text data which we might think not of privacy concern would majorly reveal a lot about the patient's integrity and personal life. Clinical data not only have patient demographic data but lot of hidden data which might go unseen and thus could arise privacy issues. Bi‐LSTM with attention layer is also added on top to realize the importance of critical words which will be of great importance in terms of classification, we are able to achieve accuracy of about 92%. About 206,926 sentences are used out of which 80% are used for training and rest for testing we get accuracy of 90% approx. with Bi‐LSTM alone. The same set of datasets is used for BERT model with accuracy of 93% approx. [ABSTRACT FROM AUTHOR]

Although the market demand for smart devices (SDs) in the Internet of Things (IoT) era is surging, the corresponding thunderstorm protection measures have rarely attracted attention. This paper presents a thunderstorm prediction method with elevation correction, to reduce the thunderstorm damage to SDs by visually tracking thunderstorm activities. First, a self‐made three‐dimensional atmospheric electric field apparatus (3DAEFA) deployed in IoT is developed to collect real‐time AEF data. A 3DAEFA‐based localization model is established, and the localization formula after correction is derived. AEF data predicted by the bi‐directional long short‐term memory (BiLSTM) model are input to this formula to obtain thunderstorm point charge localization results. Then, the localization skill is evaluated. Finally, the proposed method is assessed in experiments, under single and multiple point charge conditions. There are significant reductions of at least 33.1% and 8.8% in ranging and elevation angle errors, respectively. Particularly, this post‐prediction correction reduces the deviation of fitted point charge moving paths by at most 0.189 km, demonstrating excellent application effects. Comparisons with radar charts and existing methods testify that this method can effectively predict thunderstorms. [ABSTRACT FROM AUTHOR]

Radiologists and clinicians must automatically examine breast and tumor locations and sizes accurately. In recent years, several neural network‐based feature fusion versions have been created to improve medical image segmentation. Multi‐modal image fusion photos may efficiently identify tumors. This work uses image fusion to identify computed tomography and magnetic resonance imaging alterations. A Gauss‐log ratio operator is recommended for difference image production. The Gauss‐log ratio and log ratio difference image complement the objective of improving the difference map through image fusion. The feature change matrix extracts edge, texture, and intensity from each picture pixel. The final change detection map classifies feature vectors as "changed" or "unchanged" which has been mapped for high‐resolution or low‐resolution pixels. This paper proposes a multi‐feature blocks (MFB) based neural network for multi‐feature fusion. This neural network modeling approach globalizes pixel spatial relationships. MFB‐based feature fusion also aims to capture channel interactions between feature maps. The proposed technique outperforms state‐of‐the‐art approaches which have been discussed in detail in experimental results section. [ABSTRACT FROM AUTHOR]

Cost‐sensitive ensemble learning as a combination of two approaches, ensemble learning and cost‐sensitive learning, enables generation of cost‐sensitive tree‐based ensemble models using the cost‐sensitive decision tree (CSDT) learning algorithm. In general, tree‐based models characterize nice graphical representation that can explain a model's decision‐making process. However, the depth of the tree and the number of base models in the ensemble can be a limiting factor in comprehending the model's decision for each sample. The CSDT models are widely used in finance (e.g., credit scoring and fraud detection) but lack effective explanation methods. We previously addressed this gap with cost‐sensitive tree Shapley Additive Explanation Method (CSTreeSHAP), a cost‐sensitive tree explanation method for the single‐tree CSDT model. Here, we extend the introduced methodology to cost‐sensitive ensemble models, particularly cost‐sensitive random forest models. The paper details the theoretical foundation and implementation details of CSTreeSHAP for both single CSDT and ensemble models. The usefulness of the proposed method is demonstrated by providing explanations for single and ensemble CSDT models trained on well‐known benchmark credit scoring datasets. Finally, we apply our methodology and analyze the stability of explanations for those models compared to the cost‐insensitive tree‐based models. Our analysis reveals statistically significant differences between SHAP values despite seemingly similar global feature importance plots of the models. This highlights the value of our methodology as a comprehensive tool for explaining CSDT models. [ABSTRACT FROM AUTHOR]

To address the issue of low efficiency caused by the repeated use of quantum attack resistant static identity authentication methods in a satellite integrated smart grid, this paper proposes a quantum attack resistant continuous identity authentication protocol. First, in the initial authentication stage, in order to reduce computational complexity, the key encryption mechanism in the CRYSTALS‐Kyber algorithm was improved and combined with the NTRU message recovery digital signature scheme to construct a lattice based explicit AKE (Kyber NTRU. AKE), which achieved mutual authentication and negotiated shared tokens. Second, in the continuous authentication stage, incorporating quantum attack resistant tokens into the current algorithm to improve authentication efficiency. The formal analysis results indicate that compared to the weakly forward secure Kyber.AKE in the CRYSTALS‐Kyber algorithm, Kyber‐NTRU.AKE achieves complete forward secrecy, while the non‐formal analysis results demonstrate the security of the continuous authentication phase. Through theoretical analysis and efficiency comparison with Cyber.AKE, the analysis shows that the Cyber‐NTRU.AKE has higher computational and communication efficiency than Cyber.AKE. [ABSTRACT FROM AUTHOR]

Latent Dirichlet allocation (LDA) is one of the major models used for topic modelling. A number of models have been proposed extending the basic LDA model. There has also been interesting research to replace the Dirichlet prior of LDA with other pliable distributions like generalized Dirichlet, Beta‐Liouville and so forth. Owing to the proven efficiency of using generalized Dirichlet (GD) and Beta‐Liouville (BL) priors in topic models, we use these versions of topic models in our paper. Furthermore, to enhance the support of respective topics, we integrate mixture components which gives rise to generalized Dirichlet mixture allocation and Beta‐Liouville mixture allocation models respectively. In order to improve the modelling capabilities, we use variational inference method for estimating the parameters. Additionally, we also introduce an online variational approach to cater to specific applications involving streaming data. We evaluate our models based on its performance on applications related to text classification, image categorization and genome sequence classification using a supervised approach where the labels are used as an observed variable within the model. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) autonomous driving technology based on deep learning has achieved great success. However, under the tunnel environment, the computer vision‐based IoV may fail due to low illumination. In order to handle this issue, this paper deploys an image enhancement module at the terminal of the IoV to alleviate the low illumination influence. The enhanced images can be submitted through IoT to the cloud server for further processing. The core algorithm of image enhancement is implemented by a dynamic graph embedded transformer network based on federated learning which can fully utilize the data resources of multiple devices in IoV and improve the generalization. Extensive comparative experiments are conducted on the publicly available dataset and the self‐built dataset which is collected under the tunnel environment. Compared with other deep models, all results confirm that the proposed graph embedded Transformer model can effectively enhance the detail information of the low‐light image, which can greatly improve the following tasks in IoV. [ABSTRACT FROM AUTHOR]

The existing YOLOv5‐based framework has achieved great success in the field of target detection. However, in forest fire detection tasks, there are few high‐quality forest fire images available, and the performance of the YOLO model has suffered a serious decline in detecting small‐scale forest fires. Making full use of context information can effectively improve the performance of small target detection. To this end, this paper proposes a new graph‐embedded YOLOv5 forest fire detection framework, which can improve the performance of small‐scale forest fire detection using different scales of context information. To mine local context information, we design a spatial graph convolution operation based on the message passing neural network (MPNN) mechanism. To utilize global context information, we introduce a multi‐head self‐attention (MSA) module before each YOLO head. The experimental results on FLAME and our self‐built fire dataset show that our proposed model improves the accuracy of small‐scale forest fire detection. The proposed model achieves high performance in real‐time performance by fully utilizing the advantages of the YOLOv5 framework. [ABSTRACT FROM AUTHOR]

This article describes experiments on speech segmentation using long short‐term memory recurrent neural networks. The main part of the paper deals with multi‐lingual and cross‐lingual segmentation, that is, it is performed on a language different from the one on which the model was trained. The experimental data involves large Czech, English, German, and Russian speech corpora designated for speech synthesis. For optimal multi‐lingual modeling, a compact phonetic alphabet was proposed by sharing and clustering phones of particular languages. Many experiments were performed exploring various experimental conditions and data combinations. We proposed a simple procedure that iteratively adapts the inaccurate default model to the new voice/language. The segmentation accuracy was evaluated by comparison with reference segmentation created by a well‐tuned hidden Markov model‐based framework with additional manual corrections. The resulting segmentation was also employed in a unit selection text‐to‐speech system. The generated speech quality was compared with the reference segmentation by a preference listening test. [ABSTRACT FROM AUTHOR]

Traffic detection (including lane detection and traffic sign detection) is one of the key technologies to realize driving assistance system and auto drive system. However, most of the existing detection methods are designed based on single‐modal visible light data, when there are dramatic changes in lighting in the scene (such as insufficient lighting in night), it is difficult for these methods to obtain good detection results. In view of multi‐modal data can provide complementary discriminative information, based on the YoLoV5 model, this paper proposes a multi‐modal fusion YoLoV5 network, which consists of three key components: the dual stream feature extraction module, the correlation feature extraction module, and the self‐attention fusion module. Specifically, the dual stream feature extraction module is used to extract the features of each of the two modalities. Secondly, input the features learned from the dual stream feature extraction module into the correlation feature extraction module to learn the features with maximum correlation. Then, the extracted maximum correlation features are used to achieve information exchange between modalities through a self‐attention mechanism, and thus obtain fused features. Finally, the fused features are inputted into the detection layer to obtain the final detection result. Experimental results on different traffic detection tasks can demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, graph neural networks (GNNs) have attracted much attention in the field of machine learning due to their remarkable success in learning from graph‐structured data. However, implementing GNNs in practice faces a critical bottleneck from the high complexity of communication and computation, which arises from the frequent exchange of graphic data during model training, especially in limited communication scenarios. To address this issue, we propose a novel framework of federated graph neural networks, where multiple mobile users collaboratively train the global model of graph neural networks in a federated way. The utilization of federated learning into the training of graph neural networks can help reduce the communication overhead of the system and protect the data privacy of local users. In addition, the federated training can help reduce the system computational complexity significantly. We further introduce a greedy‐based user selection for the federated graph neural networks, where the wireless bandwidth is dynamically allocated among users to encourage more users to attend the federated training of neural networks. We perform the convergence analysis on the federated training of neural networks, in order to obtain some more insights on the impact of critical parameters on the system design. Finally, we perform the simulations on the coriolis ocean for reAnalysis (CORA) dataset and show the advantages of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Visual question answering (VQA) is a challenging task in computer vision. Recently, there has been a growing interest in text‐based VQA tasks, emphasizing the important role of textual information for better understanding of images. Effectively utilizing text information within the image is crucial for achieving success in this task. However, existing approaches often overlook the contextual information and neglect to utilize the relationships between scene‐text tokens and image objects. They simply incorporate the scene‐text tokens mined from the image into the VQA model without considering these important factors. In this paper, the proposed model initially analyzes the image to extract text and identify scene objects. It then comprehends the question and mines relationships among the question, OCRed text, and scene objects, ultimately generating an answer through relational reasoning by conducting semantic and positional attention. Our decoder with attention map loss enables prediction of complex answers and handles dynamic vocabularies, reducing decoding space. It outperforms softmax‐based cross entropy loss in accuracy and efficiency by accommodating varying vocabulary sizes. We evaluated our model's performance on the TextVQA dataset and achieved an accuracy of 53.91% on the validation set and 53.98% on the test set. Moreover, on the ST‐VQA dataset, our model obtained ANLS scores of 0.699 on the validation set and 0.692 on the test set. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid blockchain‐based authentication scheme is proposed that provides the mechanism to authenticate the randomly distributed sensor IoTs. These nodes are divided into three types: ordinary nodes, cluster heads and sink nodes. For authentication of these nodes in a Wireless Sensor IoTs (WSIoTs), a hybrid blockchain model is introduced. It consists of both private and public blockchains, which are used to authenticate ordinary nodes and cluster heads, respectively. Moreover, to handle the issue of cluster head failure due to inefficient energy consumption, Improved Heterogeneous Gateway‐based Energy‐Aware Multi‐hop Routing (I‐HMGEAR) protocol is proposed in combination with blockchain. It provides a mechanism to efficiently use the overall energy of the network. Besides, the processed data of subnetworks is stored on blockchain that causes the issue of increased monetary cost. To solve this issue, an external platform known as InterPlanetary File System (IPFS) is used, which distributively stores the data on different devices. The simulation results show that our proposed model outperforms existing clustering scheme in terms of network lifetime and data storage cost of the WSIoTs. Our proposed scheme increases the lifetime of the network as compared to existing trust management model, intrusion prevention and multi WSN authentication schemes by 17.5%, 24.2% and 19.6%, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, manual event assignment for Chinese mayor's hotline is still a problem of low efficiency. In this paper, we propose a computer‐aided event assignment method based on hierarchical features and enhanced association. First, hierarchical features of hotline events are extracted to obtain event encoding vectors. Second, the fine‐tuned RoBERTa2RoBERTa model is used to encode the "sanding" responsibility texts of Chinese local departments. Third, an association enhanced attention (AEA) mechanism is proposed to capture the correlation information of the "event‐sanding" splicing vectors for the sake of obtaining matching results of "event‐sanding," and the matching results are input into the classifier. Finally, the assignment department for is obtained by a department selection module. Experimental results show that our method can achieve better performance compared with several baseline methods on HEAD (a dataset we construct independently). The ablation experiments also demonstrate the validity of each key module in our method. [ABSTRACT FROM AUTHOR]

Emotional recognition in conversations (ERC) is increasingly being applied in various IoT devices. Deep learning‐based multimodal ERC has achieved great success by leveraging diverse and complementary modalities. Although most existing methods try to adopt attention mechanisms to fuse different information, these methods ignore the complementarity between modalities. To this end, the joint cross‐attention model is introduced to alleviate this issue. However, multi‐scale feature information on different modalities is not utilized. Moreover, the context relationship plays an important role in feature extraction in the expression recognition task. In this paper, we propose a novel joint hierarchical graph convolution network (JHGCN) which exploits different layer features and context relationships for facial expression recognition based on audio‐visual (A‐V) information. Specifically, we adopt different deep networks to extract features from different modalities individually. For V modality, we construct V graph data based on patch embeddings which are extracted from the transformer encoder. Moreover, we embed the graph convolution which can leverage the intra‐modality relationships with the transformer encoder. Then, the deep feature from different layers is fed to the hierarchical fusion module to enhance feature representation. At last, we use the joint cross‐attention mechanism to exploit the complementary inter‐modality relationships. To validate the proposed model, we have conducted various experiments on the AffWild2 and CMU‐MOSI datasets. All results confirm that our proposed model achieves highly promising performance compared to the joint cross‐attention model and other methods. [ABSTRACT FROM AUTHOR]

Stock prices forecasting has always been a challenging task. Although many research projects adopt machine learning and deep learning algorithms to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper we introduce a hybrid model that improves stock price prediction by emphasizing the dependencies between adjacent stock prices. The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to identify dependencies between stock prices across time windows, while LSTM analyses the initial time‐series data with the combination of dependencies which considered as residuals. In predicting the SSE Composite Index, our experiment reveals that when the closing price data for the previous five consecutive trading days is used as the input, the performance of the model (ResNLS‐5) is optimal compared to those with other inputs. Furthermore, ResNLS‐5 outperforms vanilla CNN, RNN, LSTM, and BiLSTM models in terms of prediction accuracy. It also demonstrates at least a 20% improvement over the current state‐of‐the‐art baselines. To verify whether ResNLS‐5 can help clients effectively avoid risks and earn profits in the stock market, we construct a quantitative trading framework for back testing. The experimental results show that the trading strategy based on predictions from ResNLS‐5 can successfully mitigate losses during declining stock prices and generate profits in the periods of rising stock prices. [ABSTRACT FROM AUTHOR]

The real‐time tracking process of dim targets in space is mainly achieved through the correlation and prediction of dots after the detection and calculation process. The on‐board calculation of the tracking needs to be completed in milliseconds, and it needs to reach the microsecond level at high frame rates. For real‐time tracking of dim targets in space, it is necessary to achieve universal tracking calculation acceleration in response to different space regions and complex backgrounds, which poses high requirements for engineering implementation architecture. This paper designs a Kalman filter calculation based on digital logic parallel acceleration architecture for real‐time solution of dim target tracking on‐board. A unified architecture of Vector Processing Element (VPE) was established for the calculation of Kalman filtering matrix, and an array computing structure based on VPE was designed to decompose the entire filtering process and form a parallel pipelined data stream. The prediction errors under different fixed point bit widths were analyzed and deduced, and the guidance methods for selecting the optimal bit width based on the statistical results were provided. The entire design was engineered based on Xilinx's XC7K325T, resulting in an energy efficiency improvement compared to previous designs. The single iteration calculation time does not exceed 0.7 microseconds, which can meet the current high frame rate target tracking requirements. The effectiveness of this design has been verified through simulation of random trajectory data, which is consistent with the theoretical calculation error. [ABSTRACT FROM AUTHOR]

Skin cancer, also known as melanoma, is a deadly form of skin cancer that can significantly improve survival rates when diagnosed at an early stage. It is usually diagnosed visually from dermoscopic images, and such visual assessment of skin cancer by the naked eye is a challenging and arduous task. Therefore, the detection of melanoma from dermoscopic images using trained artificial intelligence models is of great importance today. However, since melanoma is a rare disease, existing databases of skin lesions often contain highly unbalanced numbers of benign and malignant samples. In this paper, we propose a new one‐class classification‐based skin lesion classification strategy for small and unbalanced datasets. One‐class classification (OCC) is a special case of multi‐classification. OCC aims to learn a descriptive paradigm from positive class data (true data) during training and reject pseudo data (fake data) that do not conform to the paradigm during inference. OCC has great potential for application in anomaly detection problems. We have analyzed several approaches to the OCC task in recent years and propose a new design paradigm for the OCC problem, taking into account the unbalanced data set of the melanoma classification task. We have designed an improved OCC network based on this design paradigm, where the network is based on the architecture of a residual neural network, combining the coding and decoding idea of variational self‐encoder and the adversarial training idea of an adversarial neural network, using binary cross‐entropy as the loss function and introducing the channel attention mechanism. Tests on several publicly available dermatology datasets show that this improved OCC network addresses the unbalanced dataset situation in melanoma image classification to some extent while having relatively excellent performance. Compared with some traditional networks, it can obtain more stable training results and perform more consistently on complex datasets. [ABSTRACT FROM AUTHOR]

With the surge of searching and reading online health‐based articles, maintaining the quality and credibility of online health‐based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate the assessment and scoring of online health‐related articles' credibility. The paper proposed an Attention‐based Recurrent Multichannel Convolutional Neural Network (ARMCNN) model. The proposed model incorporates a BiLSTM layer, a multichannel CNN layer, and an attention layer and predicts the credibility of online health information. To perform a reliable evaluation of the presented model, we utilize the health articles reviewed by the experts, compiled in a labeled dataset termed "Pubhealth," which consists of thousands of health articles. The results are evaluated using five performance measures, accuracy, precision, recall, f1‐score, and area under the ROC curve (AUC). Furthermore, we extensively compared the proposed model with different deep learning and machine learning models such as Long short‐term memory (LSTM), Bidirectional LSTM, CNN (Convolutional neural network), and RNN‐CNN. The experimental results showed that the proposed model produced state‐of‐the‐art performance on the used dataset by achieving an accuracy of 0.88, precision of 0.92, recall of 0.87, f1‐score of 0.90, and AUC of 0.94. Further, the proposed model yielded better performance than other benchmarked techniques for the credibility assessment of online health articles. [ABSTRACT FROM AUTHOR]

Text classification (TC) is a very crucial task in this century of high‐volume text datasets. Feature selection (FS) is one of the most important stages in TC studies. In the literature, numerous feature selection methods are recommended for TC. In the TC domain, filter‐based FS methods are commonly utilized to select a more informative feature subsets. Each method uses a scoring system that is based on its algorithm to order the features. The classification process is then carried out by choosing the top‐N features. However, each method's feature order is distinct from the others. Each method selects by giving the qualities that are critical to its algorithm a high score, but it does not select by giving the features that are unimportant a low value. In this paper, we proposed a novel filter‐based FS method namely, brilliant probabilistic feature selector (BPFS), to assign a fair score and select informative features. While the BPFS method selects unique features, it also aims to select sparse features by assigning higher scores than common features. Extensive experimental studies using three effective classifiers decision tree (DT), support vector machines (SVM), and multinomial naive bayes (MNB) on four widely used datasets named Reuters‐21,578, 20Newsgroup, Enron1, and Polarity with different characteristics demonstrate the success of the BPFS method. For feature dimensions, 20, 50, 100, 200, 500, and 1000 dimensions were used. The experimental results on different benchmark datasets show that the BPFS method is more successful than the well‐known and recent FS methods according to Micro‐F1 and Macro‐F1 scores. [ABSTRACT FROM AUTHOR]

Persistent homology is a computationally intensive and yet extremely powerful tool for Topological Data Analysis. Applying the tool on potentially infinite sequence of data objects is a challenging task. For this reason, persistent homology and data stream mining have long been two important but disjoint areas of data science. The first computational model, that was recently introduced to bridge the gap between the two areas, is useful for detecting steady or gradual changes in data streams, such as certain genomic modifications during the evolution of species. However, that model is not suitable for applications that encounter abrupt changes of extremely short duration. This paper presents another model for computing persistent homology on streaming data that addresses the shortcoming of the previous work. The model is validated on the important real‐world application of network anomaly detection. It is shown that in addition to detecting the occurrence of anomalies or attacks in computer networks, the proposed model is able to visually identify several types of traffic. Moreover, the model can accurately detect abrupt changes of extremely short as well as longer duration in the network traffic. These capabilities are not achievable by the previous model or by traditional data mining techniques. [ABSTRACT FROM AUTHOR]

Lifelong reinforcement learning is able to continually accumulate shared knowledge by estimating the inter‐task relationships based on training data for the learned tasks in order to accelerate learning for new tasks by knowledge reuse. The existing methods employ a linear model to represent the inter‐task relationships by incorporating task features in order to accomplish a new task without any learning. But these methods may be ineffective for general scenarios, where linear models build inter‐task relationships from low‐dimensional task features to high‐dimensional policy parameters space. Also, the deficiency of calculating errors from objective function may arise in the lifelong reinforcement learning process when some errors of policy parameters restrain others due to inter‐parameter correlation. In this paper, we develop a policy generation network that nonlinearly models the inter‐task relationships by mapping low‐dimensional task features to the high‐dimensional policy parameters, in order to represent the shared knowledge more effectively. At the same time, we propose a novel objective function of lifelong reinforcement learning to relieve the deficiency of calculating errors by adding weight constraints for errors. We empirically demonstrate that our method improves the zero‐shot policy performance across a variety of dynamical systems. [ABSTRACT FROM AUTHOR]

Alzheimer's disorder (AD) causes permanent impairment in the brain's memory of the cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease in the initial stages is challenging for researchers. Deep learning and machine learning‐based techniques can help resolve many issues associated with brain imaging exploration. Brain MR Images (Brain‐MRI) are used to detect Alzheimer's in computable research work. To correctly categorize the stages of Alzheimer's disease, discriminative features need to be extracted from the MR images. Recently, many studies have used deep learning methods for the early detection of this disorder. However, overfitting degrades the deep learning method's performance because the dataset's selection images are smaller and imbalanced. Some studies could not reach more discriminative and effectual attention‐aware features for Alzheimer's stage classification to increase the model performance. In this paper, we develop a novel hierarchical residual attention learning‐inspired multistage conjoined twin network (HRAL‐CTNN) to classify the stages of Alzheimer's. We used augmentation approaches to scale insufficient and imbalanced data. The HRAL‐CTNN is efficiently overcoming the issues of not obtaining efficient attention‐aware and generative features for Alzheimer's stage classification. The proposed model solved the problem of redundant features by extracting attentive discriminant features, and scaling imbalance data by data augmentation, after that training and validation using HRAL‐CTNN. The execution of this proposed work has been performed on the ADNI MRI dataset. This work achieved outstanding accuracy of 99.97 ±$$ \pm $$ 0.01% and F1 score of 99.30 ±$$ \pm $$ 0.02% for Alzheimer's stage classification. This model proposed by our group outperformed the existing related studies in terms of the model's performance score. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one on the efficient grid scheduling, and another on planning for natural language generation (LNG).

With a broad usage of digital imaging data in hospitals, the medical image repository size is growing in a very rapid manner. This creates difficulty in querying and managing these vast databases that in turn leads to the requirement of Content‐Based Medical Image Retrieval (CBMIR) systems. The CBMIR is considered as the major ambiguous and challenging task for reducing the semantic gap among the human queries and images in the datasets having more information content. The main intention of this paper is to enhance medical image retrieval and classification using the improved pattern descriptor and deep learning. For the medical image retrieval, this paper develops the Optimized Local Directional Weber Pattern with a multi‐objective similarity function. This similarity function focuses on the measures like Structural Similarity Index Measure, Peak Signal‐to‐Noise Ratio, Mean Squared Error MSE, and correlation. Likewise, the development of an improved Faster‐Region Convolutional Neural Network (Faster‐RCNN) is adopted in the classification phase. Both the retrieval and classification are enhanced by the Modified Wind Speed‐based Deer Hunting Optimization Algorithm. Considering classification and retrieval tasks, the experiments on benchmark datasets reveal an enhancement from the introduced algorithm when compared over the existing methods. [ABSTRACT FROM AUTHOR]

Anomaly detection is a key step in ensuring the security and reliability of large‐scale distributed systems. Analyzing system logs through artificial intelligence methods can quickly detect anomalies and thus help maintenance personnel to maintain system security. Most of the current works only focus on the temporal or spatial features of distributed system logs, and they cannot sufficiently extract the global features of distributed system logs to achieve a good correct rate of anomaly detection. To further address the shortcomings of existing methods, this paper proposes a deep learning model with global spatiotemporal features to detect the presence of anomalies in distributed system logs. First, we extract semi‐structured log events from log templates and model them as natural language. In addition, we focus on the temporal characteristics of logs using the bidirectional long short‐term memory network and the spatial invocation characteristics of logs using the Transformer. Extensive experimental evaluations show the advantages of our proposed model for distributed system log anomaly detection tasks. The optimal F1‐Score on three open‐source datasets and our own collected distributed system datasets reach 98.04%, 94.34%, 88.16%, and 97.40%, respectively. [ABSTRACT FROM AUTHOR]

Random minimaxing studies the consequences of using a random number for scoring the leaf nodes of a full width game tree and then computing the best move using the standard minimax procedure. Experiments in Chess showed that the strength of play increases as the depth of the lookahead is increased. Previous research by the authors provided a partial explanation of why random minimaxing can strengthen play by showing that, when one move dominates another move, then the dominating move is more likely to be chosen by minimax. This paper examines a special case of determining the move probability when domination does not occur. Specifically, we show that, under a uniform branching game tree model, whether the probability that one move is chosen rather than another depends not only on the branching factors of the moves involved, but also on whether the number of ply searched is odd or even. This is a new type of game tree pathology, where the minimax procedure will change its mind as to which move is best, independently of the true value of the game, and oscillate between moves as the depth of lookahead alternates between odd and even. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta‐learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F‐score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate. [ABSTRACT FROM AUTHOR]

In film and television animation works, animated characters are the soul and core of the work. The behavior, language expression, and emotional expression of animated characters play an important role in the expression of the animation theme and content. Aiming at the problem that the mobile animation system can only add and change actions for a single virtual character, and the characters cannot interact with each other, this paper analyzes the technical principles, technical characteristics, and application scope of human–computer interaction (HCI), taking sensors as the research object. An algorithm for separating the human body from the background environment in the depth image is proposed. Through the calculation of the depth value, the calculation results are compared, and the target human body and the background are effectively separated. In the depth data processing, the algorithm of judging the pixel offset value is used to identify the body part, and a sensor‐based HCI system is designed. The depth‐of‐field data map acquired by the sensor is used to identify human body parts and determine actions, thereby realizing HCI based on action recognition. Simulation test results show that the effective rate of the system is 80%, and the design of animated characters can be put into the visualization stage. Using the algorithm in this paper, the physical signs of the animated characters can be quickly identified, so that the next action of the animation can be more clearly captured. Has a certain practical value. [ABSTRACT FROM AUTHOR]

The smart grid communication parties need to process the data by the trusted central node, which will lead to security issues such as single‐point attacks and data tampering. This paper proposes a smart grid data access control scheme based on blockchain, the user completes the registration of the smart meter by three encryptions. After the registration is completed, the registration information will be uploaded to the blockchain. In the data access phase, the verification center verifies the user's data access request, the database will accept the user's request for data if the verification is passed, and that will be broadcasted on the entire network and uploaded to the blockchain. The security of the scheme is analyzed by using a random oracle model. Analysis shows that this scheme can resist public key replacement attacks and malicious key generation center (KGC) attacks. Compared with the existing scheme, this scheme can more effectively resist more types of attacks. It shows that the smart grid data access control scheme proposed in this paper is safe, reliable and efficient. [ABSTRACT FROM AUTHOR]

Role Back Access Control model (RBAC) allows devices to access cloud services after authentication of requests. However, it does not give priority in Big Data to devices located in certain geolocations. Regarding the crisis in a specific region, RBAC did not provide a facility to give priority access to such geolocations. In this paper, we planned to incorporate Location Time‐ (GEOTime) based condition alongside Priority Attribute role‐based access control model (PARBAC), so requesters can be allowed/prevented from access based on their location and time. The priority concept helped to improve the performance of the existing access model. TIME‐PARBAC also ensures service priorities based on geographical condition. For this purpose, the session is encrypted using a secret key. The secret key is created by mapping location, time, speed, acceleration and other information into a unique number, that is, K(Unique_Value) = location, time, speed, accelerator, other information. Spatial entities are used to model objects, user position, and geographically bounded roles. The role is activated based on the position and attributes of the user. To enhance usability and flexibility, we designed a role schema to include the name of the role and the type of role associated with the logical position and the rest of the PARBAC model proposed using official documentation available on the website for Azure internet of things (IoT) Cloud. The implementation results utilizing a health use case signified the importance of geology, time, priority and attribute parameters with supporting features to improve the flexibility of the existing access control model in the IoT Cloud. [ABSTRACT FROM AUTHOR]

The educational sector faces a new dimension that is dominated by lifelong learning and is affected by the technical, social, and cultural changes. This pattern represents the need to improve the teaching methods for physical education and sports science. The use of computers and other information technology to increase the effectiveness of the teaching process is a modern method. This paper aims to illustrate the use of information and communication technologies (ICT) in physical education and sports. In our field, the gradual computerization results can be summed up in the following aspects: education software, design, and planning activities, recording outcomes, motion monitoring, video analysis, comparison of performance and synchronizing, measurements at distance and time and the evaluation of the activity. Although physical education and sports are practical activities, specialists can make use of modern teaching technologies. In this paper, the system of curriculum assessment for physical education has been analyzed and researched in computer assessment. The first section introduced the method of assessment of the physical education program. The second phase of the paper represents a teaching model of the physical education mathematical model utilizing the Comprehensive Adaptive Fuzzy Evaluation Theory has been proposed. A new level is the modernization of physics education with the artificial intelligence computer education system built in this paper. The experimental results have high performance in detecting the physical activity of college students. [ABSTRACT FROM AUTHOR]

This paper reports on our study of a sparse neural network regression method based on a single hidden layer architecture and sparsity‐inducing penalties. To determine the size of network in a data‐adaptive way, we adopt the lasso and group lasso penalty functions to simultaneously induce sparsity at node and predictor levels. We also devise several techniques to improve performance of the neural network regression estimator. We adopt a B‐spline activation function with a compact support to identify local trends of data. In addition, we develop an algorithm based on node addition process, in which additional nodes come into the network as a complexity parameter decreases. At each value of the complexity parameter, the algorithm initializes the additional nodes along the direction that best captures the unexplained functional relationship between the response and predictors. The optimization step is conducted based on an efficient coordinate descent algorithm. Numerical studies based on simulated and real datasets illustrate that the combination of the aforementioned devices significantly improves the performance of the neural network estimator. [ABSTRACT FROM AUTHOR]

Time Series Classification (TSC) started getting a lot of attention recently, mostly due to the important real‐world applications of time series such as in the financial industry. In this context, various approaches have been proposed to treat this important and challenging problem in data mining. These approaches include deep learning models that are outperformed the traditional classification techniques. However, the choice of an optimal and efficient deep neural network for TSC is still a major problem. Motivated by this challenge, we propose in this paper, an optimized deep learning framework for TSC tasks, using normal cloud representation and convolutional neural networks optimization (NCR‐CNNO). Our approach consists of two phases: In the first one, we convert the raw time series to a matrix of characteristics using two‐dimensional normal cloud representation. In the second phase, we suggest a CNN to handle the matrix generated in the first phase, as well as, an optimization model is proposed to optimize the unnecessary and redundant parameters. Experiments conducted on extensive time series datasets demonstrate that NCR‐CNNO yields significant improvement in the performance of time series classification compared to the state of the art. [ABSTRACT FROM AUTHOR]

Stock forecasting is difficult because of its complexity and uncertainty. To better predict stock prices and then provide stockholders with reasonable suggestions, this paper proposes an improved time convolution network (TCN) model for predicting stock prices. The model used can make up for some of the shortcomings of the traditional neural network, use the trading data in the stock market, and put the preprocessed data of financial news into the model for training to improve the accuracy of prediction. Using the Shanghai Securities Exchange (SSE) 50 Index (Shanghai Securities Exchange 50 Index selects the most representative 50 stocks with large scale and good liquidity in Shanghai stock market as sample stocks) and news text crawled from financial web pages as samples, predict the direction of the SSE 50 Index's rise and fall. After using different network structure hyperparameters to adjust the model structure, the prediction effect is compared with other models, and it is found that the proposed improved TCN model can effectively improve the effect of predicting the rise and fall of the SSE 50 index, and can complete the model training and predict the stock price faster. [ABSTRACT FROM AUTHOR]

Recently, intrusion detection system (IDS) has become an essential remedy to protect networks from malicious activities and attacks. The attacks force the network activities under risk and cause network data as an unrecoverable one. The attacks have different features in nature and their detection rate is low. An efficient attack detection system requires a huge volume of network transactions and relevant features for training. But the existing datasets have significant and nonsignificant features together. Hence, the significant feature selection plays a major role to detect the attacks. For this reason, this paper adopts the XGBoost approach to identify the relevant (significant) features of the attacks in the dataset. After this XGBoost, this article applies the bidirectional long short term model (Bi‐LSTM) for the detection and classification of the attacks. This Bi‐LSTM model is a novel approach to the IDS and very effective to increase the detection rate. Thus, this article focuses on feature reduction and classification of the attacks. The experimental results show that the proposed XGBoost and Bi‐LSTM combination outperforms in detecting attacks. [ABSTRACT FROM AUTHOR]

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance. [ABSTRACT FROM AUTHOR]

The paper describes our current research activities and results related to developing knowledge‐based systems to support the creation of entity‐relationship (ER) models. The authors based obtaining an ER model in textual form on translation from one language into another, that is, from an English controlled natural language into the formalized language of an ER data model. Our translation method consisted of creating translation rules of sentential form parts into ER model constructs based on the textual and character patterns detected in the business descriptions. To enable the computer analyses necessary for creating translation mechanisms, we created a linguistic corpus that contains lists of the business descriptions and the texts of other business materials. From the corpus, we then created a specific dictionary and linguistic rules to automate the business descriptions' translation into the ER data model language. Before that, however, the corpus was enriched by adding annotations to the words related to ER data model constructs. In this paper, we also present the main issues uncovered during the translation process and offer a possible solution with utility evaluation: applying information‐extraction performance measures to a set of sentences from the corpus. [ABSTRACT FROM AUTHOR]

This paper investigates the semantic modeling of smart cities and proposes two ontology matching frameworks, called Clustering for Ontology Matching‐based Instances (COMI) and Pattern mining for Ontology Matching‐based Instances (POMI). The goal is to discover the relevant knowledge by investigating the correlations among smart city data based on clustering and pattern mining approaches. The COMI method first groups the highly correlated ontologies of smart‐city data into similar clusters using the generic k‐means algorithm. The key idea of this method is that it clusters the instances of each ontology and then matches two ontologies by matching their clusters and the corresponding instances within the clusters. The POMI method studies the correlations among the data properties and selects the most relevant properties for the ontology matching process. To demonstrate the usefulness and accuracy of the COMI and POMI frameworks, several experiments on the DBpedia, Ontology Alignment Evaluation Initiative, and NOAA ontology databases were conducted. The results show that COMI and POMI outperform the state‐of‐the‐art ontology matching models regarding computational cost without losing the quality during the matching process. Furthermore, these results confirm the ability of COMI and POMI to deal with heterogeneous large‐scale data in smart‐city environments. [ABSTRACT FROM AUTHOR]

Radio network planning is a core problem in cellular networks. It includes coverage, capacity and parameter planning. This paper investigates the Antenna Positioning Problem (APP) which is a main task in cellular networks planning. The aim is to find a trade‐off between maximizing coverage and minimizing costs. APP is the task of selecting a subset of potential locations where installing the base stations to cover the entire area. In theory, the APP is NP‐hard. To solve it in practice, we propose a new meta‐heuristic called Evolutionary Iterated Local Search that merges the local search method and some evolutionary operations of crossover and mutation. The proposed method is implemented and evaluated on realistic, synthetic and random instances of the problem of different sizes. The numerical results and the comparison with the state‐of‐the‐art show that the proposed method succeeds in finding good results for the considered problem. [ABSTRACT FROM AUTHOR]

In this paper, an optimal power flow solution method incorporating a cost model that associates the uncertainty‐related expense incurred with the use of renewable energy sources, viz., solar and wind, is demonstrated. Wind speed and solar radiation are assumed to follow Weibull and normal distributions and the uncertainty is simulated using Monte‐Carlo approach. Wind turbine mathematical model is used to estimate the wind generator output, while the same for solar PV is estimated using PV‐inverter models. The uncertainty‐induced opportunity cost for both the renewable sources is composed of the costs due to both power excess and deficit. These cost components are indicative of the reserve requirement and loss of benefit, due to the unavailability of the corresponding generation. This research models and integrates the opportunity costs of renewable generation into a conventional OPF formulation, which is then solved using four variants of particle swarm optimization method. Among these, mutation‐based PSO approach provided better results than others. The test system used is modified IEEE 39‐bus network and the performance of the method as well as the effect of the uncertainty cost is evaluated under multiple renewable penetration levels. The results also indicate that solar generation is preferred over wind in terms of the uncertainty cost, while the use of stochastic natured renewable systems is economically justified and preferred over thermal generators. [ABSTRACT FROM AUTHOR]

Most existing methods of multiobjective estimation of distributed algorithms apply the estimation of distribution of the Pareto‐solution on the decision space during the search and little work has proposed on making a regression‐model for representing the final solution set. Some inverse‐model‐based approaches were reported, such as inversed‐model of multiobjective evolutionary algorithm (IM‐MOEA), where an inverse functional mapping from Pareto‐Front to Pareto‐solution is constructed on nondominated solutions based on Gaussian process and random grouping technique. But some of the effective inverse models, during this process, may be removed. This paper proposes an inversed‐model based on random forest framework. The main idea is to apply the process of random forest variable importance that determines some of the best assignment of decision variables (xn) to objective functions (fm) for constructing Gaussian process in inversed‐models that map all nondominated solutions from the objective space to the decision space. In this work, three approaches have been used: classical permutation, Naïve testing approach, and novel permutation variable importance. The proposed algorithm has been tested on the benchmark test suite for evolutionary algorithms [modified Deb K, Thiele L, Laumanns M, Zitzler E (DTLZ) and Walking Fish Group (WFG)] and indicates that the proposed method is a competitive and promising approach. [ABSTRACT FROM AUTHOR]

Nowadays, most of the services from cloud are protuberant within the all commercial, public, and private areas. A primary difficulty of cloud computing system is making a virtualized environment safe from all intruders. The existing system uses signature‐based methods, which cannot provide accurate detection of malware. This paper put forward an approach to detect the malware by using the approach based on feature extraction and various classification techniques. Initially the clean files and malware files are extracted. The feature selection includes gain ratio to provide subset features. The classification is used to predict any malware that has been entered in the mobile device. In this paper, it is proposed to use the ensemble classifier which contains different kinds of classifiers such as Support Vector Machine, K‐Nearest Neighbor, and Naïve Bayes classification. These together are known as a meta classifier. These three classification methods had been used for proposed work and get the results with higher accuracy. This measures the correctness of the prediction happened using ensemble method with high precision and recall values which is specifically identifies the quality of the techniques used. [ABSTRACT FROM AUTHOR]

Precise modeling of a polymer electrolyte membrane fuel cell (PEMFC) is a crucial issue in analyzing and controlling electrical energy production. In this paper, a novel semiexperimental model is proposed for forecasting of PEMFC output voltage. As well, the coevolution ribonucleic acid genetic algorithm (coRNA‐GA) is presented as a novel estimation approach for determination of proposed model coefficients. This optimization method is motivated by the biological RNA, encodes the chromosomes by RNA nucleotide basics, and accepts a few RNA operations. This paper proposed several genetic operators to preserve the diversity of particles, and two sets from particles are chosen using various validation functions. In these two subpopulations, different evolutionary methods have been employed for balancing of seeking and extraction. Input pressure of cathode is chosen in this paper as a further parameter for modifying the depiction of concentration overvoltage (Vcon) in the case of conventional Amphlett's PEMFC system. Finally, the performance of the coRNA‐GA algorithm, as well as the precision of the obtained model, is authenticated via empirical results. Also, the obtained results are compared with some other methods, and the superiority of the proposed model is demonstrated in voltage prediction accuracy. [ABSTRACT FROM AUTHOR]