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• Variational Quantum Circuits (VQCs) play a key role in several applications. • VQCs are parameterized quantum circuits trained by using classical optimizers. • The paper proposes to apply memetic algorithms to train VQCs. • The designed memetic algorithm outperforms the state-of-the-art classical optimizers. Among the ready-to-implement quantum algorithms, Variational Quantum Circuits (VQCs) play a key role in several applications, including machine learning. Their strength lies in the use of a parameterized quantum circuit that is trained by means of an optimization algorithm run on a classical computer. In such a scenario, there is a strong need to design appropriate classical optimization schemes that deal efficiently with VQCs and pave the way for quantum advantage in machine learning. Among possible optimization schemes, those based on evolutionary computation are finding increasing interest, given the unconventional and nonanalytical nature of the problem to be solved. This paper proposes to apply memetic algorithms to train VQCs used as quantum classifiers and shows the benefits of exploiting this evolutionary optimization technique through a comparative experimental session. [ABSTRACT FROM AUTHOR]

Product recommendation systems are an important aspect of retailing because of the improved shopping experience provided for customers. Due to the wide range of products offered by retailers, recommendation systems provide an optimal approach for displaying only relevant products to customers by forming associations that exist between products. Still, it is also important to understand the characteristics of customers connected to different product associations. Conventional approaches for product recommendation systems apply association algorithms and unsupervised classification of customers based on product ratings. However, it is not clear what demographic properties of customers are linked to which different product associations. This paper applies a hybrid system of machine learning (ML) association and clustering algorithms to implement a product recommendation system that shows associations that exist in products and unique customer profiles linked to these associations. The method described in this paper is evaluated with a case of a hygiene product retailer in Austria. [ABSTRACT FROM AUTHOR]

Rao algorithms that include three algorithms are very simple and parameter-less algorithms with effective and desirable performance. This paper modifies these three algorithms, merges them, and establishes a powerful group algorithm. In the first optimization step, the suggested algorithm is tested on 30 standard CEC2014 functions with 50 dimensions to compare it with main algorithms, several well-known algorithms, and modified versions of RAO algorithm. It becomes evident in the first test that the suggested optimizer is effective, and reliable for optimization of real-parameter functions, and it has shown its superiority to original RAO algorithm and several modern and modified versions of RAO algorithms for most of the test functions and achieved more acceptable results than them. Moreover, the suggested algorithm benefits a faster convergence characteristic than original RAO algorithms. The proposed Colonial Competitive RAO (CCRAO) has been applied on five popular engineering problems and its results have been compared with those of recent papers. According to the results, CCRAO is an effective, robust, and reliable optimizer for engineering design problems and can contain all useful features of RAO algorithms altogether. CCRAO has succeeded to converge to the best solution for these engineering problems and surpasses most of the other algorithms. [ABSTRACT FROM AUTHOR]

Stock market trading is a major and predominant activity when one talks about the financial markets. With the inevitable uncertainty and volatility in the prices of the stocks, an investor keeps looking for ways to predict the future trends in order to dodge the losses and make the maximum possible profits. However, it cannot be denied that as of yet there is no such technique to predict the upcoming trends in the markets with complete accuracy, while multiple methods are being explored to improve the predictive performance of models to an extent as large as possible. With the advancement in Machine Learning (ML) and Deep Learning (DL) over the past few years, many algorithms are being deployed for stock price prediction. This paper researches 5 algorithms namely K-Nearest Neighbors, Linear Regression, Support Vector Regression, Decision Tree Regression, and Long Short-Term Memory for predicting stock prices of 12 leading companies of the Indian stock market. After exhaustive research of the various aspects related to the application of ML in stock market, a data extensive implementation has been carried out as a part of this research work wherein the stock price dataset of 12 companies over the last 7 years was collected and used. The paper also highlights some more efficient and robust techniques that are used to forecast trends in the stock market. In detail, the methodology followed, to acquire the results, has been talked about step-wise. Furthermore, a detailed comparative analysis of the performances of the aforementioned algorithms for stock price prediction has been carried out with the results displayed in a legible tabulated and graphical form to analyze them better. The conclusions from this novel, data comprehensive research work have been presented and it has been inferred that the DL algorithm outperforms all the other algorithms for stock price or time series prediction and provides results with extensive accuracy. [ABSTRACT FROM AUTHOR]

The evolution and civilization development lead us to the progress next important step - the creation of AGI. More "intelligent" tasks are successfully solved by machine learning methods using neural network algorithms. Not only the solved problems quantitative increase in the number and accuracy are important, but also a qualitative transition to the tasks of obtaining "truly" new knowledge and building "truly" new goals on their basis. This transition can serve as a criterion for determining AGI. The paper shows necessity of using existing knowledge to advance into the field of more and more diverse "truly" new knowledge. Also, the paper shows usefulness of implementing proposed ideas on a scalable hierarchical structure which has two main modes of operation: "intuition" and "thinking", and structure of vector control of these and other operation modes, similar to animal hormonal control. [ABSTRACT FROM AUTHOR]

A self-training algorithm is a classical semi-supervised learning algorithm that uses a small number of labeled samples and a large number of unlabeled samples to train a classifier. However, the existing self-training algorithms consider only the geometric distance between data while ignoring the data distribution when calculating the similarity between samples. In addition, misclassified samples can severely affect the performance of a self-training algorithm. To address the above two problems, this paper proposes a self-training algorithm based on data editing with mass-based dissimilarity (STDEMB). First, the mass matrix with the mass-based dissimilarity is obtained, and then the mass-based local density of each sample is determined based on its k nearest neighbors. Inspired by density peak clustering (DPC), this study designs a prototype tree based on the prototype concept. In addition, an efficient two-stage data editing algorithm is developed to edit misclassified samples and efficiently select high-confidence samples during the self-training process. The proposed STDEMB algorithm is verified by experiments using accuracy and F-score as evaluation metrics. The experimental results on 18 benchmark datasets demonstrate the effectiveness of the proposed STDEMB algorithm. • Mass-based dissimilarity is used to o account for the effects of data distribution. • A prototype tree was designed to efficiently identify high confidence samples. • A two-stage data editing algorithm was developed for editing misclassified samples. • Numerous experiments were conducted to prove the performance of the proposed STDEMB. [ABSTRACT FROM AUTHOR]

Twin support vector machine (TSVM) is a practical machine learning algorithm, whereas traditional TSVM can be limited for data with outliers or noises. To address this problem, we propose a novel TSVM with the symmetric LINEX loss function (SLTSVM) for robust classification. There are several advantages of our method: (1) The performance of the proposed SLTSVM for data with outliers or noise can be improved by using the symmetric LINEX loss function. (2) The introduction of regularization term can effectively improve the generalization ability of our model. (3) An efficient iterative algorithm is developed to solve the optimization problems of our SLTSVM. (4) The convergence and time complexity of the iterative algorithm are analyzed in detail. Furthermore, our model does not involve loss function parameter, which makes our method more competitive. Experimental results on synthetic, benchmark and image datasets with label noises and feature noises demonstrate that our proposed method slightly outperforms other state-of-the-art methods on most datasets. • This paper proposes SLTSVM by introducing symmetric LIENX loss function. • An interpretable method is proposed for selecting the loss parameter. • The convergence of the designed solution algorithm are further analyzed. • Experiments show that our method is efficient for data with outliers or noises. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is critically important for optimising the performance of Intrusion Detection Systems (IDSs) used in Wireless Sensor Networks (WSNs). However, selecting an optimal number of relevant features from massive IDS data sets has been an ongoing FS research problem. Many approaches use Machine Learning (ML), such as nature-inspired population-based and Differential Evolution (DE)-based algorithms for FS. However, the main drawback of DE is that it has a premature convergence issue, which leads to false convergence. In this paper, a novel mutation strategy is proposed to alleviate premature convergence and to develop a modified DE-based FS algorithm called DE with maturity extension (DE-ME). The novel mutation is achieved by changing the mutation factor in the DE\rnd\1 based on K-Nearest Neighbour (KNN) as the most efficient implementation without adding further complexity to the design process. The proposed DE-ME algorithm improves the FS operation with significant performance improvements in terms of overcoming premature convergence and increasing accuracy for selection. The DE-ME algorithm was used to select the most effective features from 41 features in the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset. The accuracy of DE-ME is high at 99.66 %, its False Positive Rate (FPR) is low at 0.464 %, and the true positive rate (TPR) achieved is 99.80 %. In addition, the number of features selected by DE-ME is 6 out of 41 features in the dataset. [ABSTRACT FROM AUTHOR]

With the fast-moving deployment of IoT (Internet of Things) applications, a large amount of data is generated and the data is in need for processing to be able contribute to the user's requirements. All of the IoT applications require low latency and certain bandwidth to provide us with new features which are Time-Dependent. Thus, it would be advantageous to redesign the current IoT architecture by understanding the unique properties of communications and computational power to support intelligent processing of IoT data and its analysis. The paper is focused on topics such as characteristics of IoT data, trends in IoT network architecture, problems in IoT data analysis and their solutions. Thus, in particular, the paper is a discussion that Edge software-defined computing and CEP (Complex Event Processing) based fog computing as a architecture that promises to support the different requirements of IoT data analytics. And also, the paper is a discussion about how machine learning, deep learning are useful in removing data processing anomalies. The leader node election for data processing at cloud is also a time consuming task, the proposed genetic based Genetic based Leader Election Algorithm (GLEA) algorithm in the paper show promising results for the solution. [ABSTRACT FROM AUTHOR]

Clustering is one of the most common technologies in data mining. The size, dimension and sparsity of data are all different aspects that restrict clustering analysis. Clustering machine learning is called unsupervised learning, which is different from classification in that the data objects used in clustering analysis have no class marks, so they need to be calculated by the clustering learning algorithm, while the classified data objects need class marks. At present, most of the sparse data algorithms with high attribute dimensions are oriented to binary data, and there is no evaluation method of clustering results, which brings great limitations to their application. The BP neural network, RBF neural network and Elman neural network model with feedback function, which are mature and widely used in pest prediction models, are studied. The purpose of this paper is to study diseases and pests based on sparse clustering algorithm. In this paper, algorithm formulas, models and data graphs are established to study. From the research, it can be seen that the damage index of pests and diseases is very high, reaching about 50. 54%. Through the research in this paper, we can know that the future will lay a certain foundation for the research of diseases and pests. [ABSTRACT FROM AUTHOR]

Achieving precision in the prediction of buildings' dynamic heat load is crucial for the advancement of smart heating systems. This research highlights the urgent need to enhance the accuracy of models predicting dynamic heat load. Through literature review, distinguished machine learning and regression algorithms were chosen to formulate prediction models. These models employ a data time-step adaptive strategy, a physics-guided loss function, and fundamental principles of heat transfer. Optimization algorithms of a mathematical nature were utilized to fine-tune the parameters and the framework of long short-term memory (LSTM) and multi-layer perceptron (MLP) models. An analytical comparison was undertaken between physics-guided models and those not guided by physics. Principal conclusions are: 1) Pelican optimization algorithm (POA)-LSTM model emerges as superior in heat load prediction accuracy of an office building, with percentage errors for actual and simulated datasets ranging from −6.7 % to 5.8 % and −5.2 %–4.5 %, respectively, and the mean absolute percentage error (MAPE) standing at 2.3 % and 1.3 %. 2) The linear regression model exhibits the lowest precision, with a MAPE of 17.5 % and 4.0 % for the 7-day prediction results in the actual and simulated datasets, respectively. These findings provide support for improving heat load prediction in heating systems. • The authors have implemented a total of 20 physically-guided models for predicting building thermal load, among which the POA-LSTM model demonstrates the highest accuracy. • The disparities in thermal load prediction accuracy between the physically-guided machine learning models and the physically-guided mathematical regression models have been identified. • The accuracy of thermal load prediction was compared between the five physical guidance models proposed in this paper and the five non-physical guidance models mentioned in other papers. [ABSTRACT FROM AUTHOR]

• Distinct DG Output Modes Validation: The simulation validates distinct DG output modes, impacting prediction. • Mode-Based State Estimation: The paper proposes an innovative mode-based state estimation using IPSO-DBSCAN clustering, overcoming k-means limitations. • Enhanced Data Recovery using DBSCAN: Enhanced data recovery employs DBSCAN with improved secondary clustering, guided by similarity and homogeneity, outperforming traditional methods. • BiGRU-PF State Estimation: BiGRU-PF enhances state estimation accuracy and robustness in distribution networks by detecting DG output modes, addressing k-means limitations, and improving data recovery. This paper proposes a new forecasting-aided state estimation (FASE) method for distribution systems that mitigates issues with uncertain distributed generation (DG) and lost measurements. We utilize an Improved Particle Swarm Optimization (IPSO)-optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for examination of historical DG output data. Based on identified DG output modes, it facilitates precise state prediction and data reconstruction,. The proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU) neural network for state prediction and a particle filter (PF) for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements. [ABSTRACT FROM AUTHOR]

The emergence of IoT has introduced new security concerns, particularly the detection of large-scale network attacks initiated by compromised IoT devices. The diverse nature of IoT devices adds complexity to attack detection, necessitating the classification of devices based on their unique characteristics and behavior patterns. This paper presents a novel device classification method grounded in traffic differentials before and after IoT devices succumb to compromise. The study defines the concept of attack sensitivity and designs a method for calculating the value. The K-means algorithm is used to classify devices into predefined categories based on their attack sensitivity values. The effectiveness of this method is demonstrated through experiments on a public dataset. Additionally, a multi-class classification model is developed to identify newly added devices to the IoT environment, with good results in accuracy, precision, recall, F1 score, and FPR (0.999, 0.997–1,0.997–1,0.998–1,0–0.001). Overall, The device classification method proposed in this paper, based on attack sensitivity, not only enables a rational definition of device categories but also ensures precise classification for devices newly integrated into the IoT environment. This lays the foundation for achieving more precise attack detection and protection, advancing the field towards enhanced cybersecurity measures. [ABSTRACT FROM AUTHOR]

This comprehensive review focuses on the Autonomous Driving System (ADS), which aims to reduce human errors that are the reason for about 95% of car accidents. The ADS consists of six stages: sensors, perception, localization, assessment, path planning, and control. We explain the main state-of-the-art techniques used in each stage, analyzing 275 papers, with 162 specifically on path planning due to its complexity, NP-hard optimization nature, and pivotal role in ADS. This paper categorizes path planning techniques into three primary groups: traditional (graph-based, sampling-based, gradient-based, optimization-based, interpolation curve algorithms), machine and deep learning, and meta-heuristic optimization, detailing their advantages and drawbacks. Findings show that meta-heuristic optimization methods, representing 23% of our study, are preferred for being general problem solvers capable of handling complex problems. In addition, they have faster convergence and reduced risk of local minima. Machine and deep learning techniques, accounting for 25%, are favored for their learning capabilities and fast responses to known scenarios. The trend towards hybrid algorithms (27%) combines various methods, merging each algorithm's benefits and overcoming the other's drawbacks. Moreover, adaptive parameter tuning is crucial to enhance efficiency, applicability, and balancing the search capability. This review sheds light on the future of path planning in autonomous driving systems, helping to tackle current challenges and unlock the full capabilities of autonomous vehicles. [ABSTRACT FROM AUTHOR]

• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

The development of stereo matching algorithm is still one of the challenging problems, especially in ill-posed regions. Hence, this article presents a survey on the algorithm frameworks related to the stereo matching algorithm. Based on the early survey that had been conducted, two major frameworks available in current stereo matching algorithm development, they are traditional and artificial intelligence (AI) frameworks. Most of the traditional methods are very low accuracy compared to the AI-based approach. This can be observed in the standard benchmarking dataset, such as from the KITTI and the Middlebury, where AI methods rank at the top of the accuracy list. Additionally, the trend for solving computer vision problems uses AI or machine learning tools that become more apparent in recent years. Thus, this paper is focusing on the survey between the deep learning frameworks, which is one of the machine learning tools related to the convolutional neural network (CNN). Several mixed approaches between CNN based method and traditional handcraft method, as well as the end to end CNN method also discussed in this paper. [ABSTRACT FROM AUTHOR]

Intelligent machines are an important carrier of artificial intelligence, which are widely used not only in the industrial field, but also in various fields of daily life and work. Path planning is an important research direction for studying machine intelligence. The main purpose is to search for a feasible path so that the robot can walk along the searched path in a complex environment. This paper aims to study the evolutionary algorithm of intelligent machines based on artificial intelligence. On the basis of introducing the characteristics of artificial potential field algorithm, the concept and characteristics of genetic algorithm, using genetic algorithm as the framework, the artificial potential field algorithm is combined with the crossover of genetic algorithm. In the operator, the optimization performance of the crossover operator is improved. The research results show that the length of the path obtained by the improved algorithm is shorter and better than that of the genetic algorithm of the single-point crossover operator. [ABSTRACT FROM AUTHOR]

This paper presents a Text similarity detection method based on artificial intelligence and natural language processing. The method combines statistical machine learning and deep learning techniques and designs six models from three perspectives: character-level, word-level, and semantic-level. These models include the diff model based on machine learning, cosine similarity model, Jaccard model, TF-IDF model, as well as the SimCSE and SBERT models based on deep learning methods. To fully leverage the characteristics of these models, three scenarios are designed to calculate the similarity scores based on experience and multiple experimental results. The results show that calculating the similarity scores using these three scenarios not only achieves high accuracy but also requires fewer computational resources. As deep learning and natural language processing technologies continue to advance, Text similarity detection methods based on artificial intelligence and natural language processing will continue to be improved and play a more significant role in practice. Future research can explore more models and algorithms to enhance the accuracy and robustness of plagiarism detection. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology, metallographic research on metal materials has made remarkable progress. In this paper, the authors processed metallographic images using machine learning algorithms to classify the different elements in the images. The classification results divide the elements into three categories, which are metal elements, non-metal elements and metal-nonmetal combination elements. On the basis of this research, a metallographic compression algorithm based on adaptive threshold is also explored, and grayscale experiments are carried out on carbon steel samples. The recognition rate is 98.3%, and the compression rate is 1.06. Experimental results verify the effectiveness of the algorithm in processing metallographic compressed images of metal materials in power grids. [ABSTRACT FROM AUTHOR]

Interactive teaching is very popular in the field of education, especially in college preschool education teaching. In this study, the experimental performance of the evaluation system based on scientific emotion construction is analyzed by the machine learning algorithm. The experimental results show that the classification model realized by the machine learning algorithm is feasible and effective for the construction of an interactive design teaching evaluation system of preschool education in colleges and universities. The main research content of this experiment is for the teaching evaluation text, this paper intends to use NB, KNN, LR, and SVM algorithms as the meta-learning algorithm in the framework, the classification effect of the meta-classifier is ranked as SVM > LR > KNN > NB. From the above data comparison, the SVM meta-classifier in the algorithm in this paper has achieved the best classification effect. The SVM meta-classifier based on the algorithm in this paper performs well, and the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the. And the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the construction of interactive teaching evaluation systems under emotional education. • The experimental results show that the classification model realized by machine learning algorithm. • This shows that this model is very suitable for studying the construction of interactive teaching. • The main research content of this experiment is for the teaching evaluation text. • This paper intends to use NB, KNN, LR and SVM algorithms as the meta-learning algorithm. [ABSTRACT FROM AUTHOR]

Advances over the past decade at the intersection of information fusion methods and Semi-Supervised Learning (SSL) are investigated in this paper that grapple with challenges related to limited labelled data. To do so, a bibliographic review of papers published since 2013 is presented, in which ensemble methods are combined with new machine learning algorithms. A total of 128 new proposals using SSL algorithms for ensemble construction are identified and classified. All the methods are categorised by approach, ensemble type, and base classifier. Experimental protocols, pre-processing, dataset usage, unlabelled ratios, and statistical tests are also assessed, underlining the major trends, and some shortcomings of particular studies. It is evident from this literature review that foundational algorithms such as self-training and co-training are influencing current developments, and that innovative ensemble techniques are continuing to emerge. Additionally, valuable guidelines are identified in the review for improving research into intrinsically semi-supervised and unsupervised pre-processing methods, especially for regression tasks. • A set of 128 recent semi-supervised ensemble methods is identified and analysed. • Semi-supervised approach, ensemble, base and preprocessing methods are identified. • The experimental protocol, settings, and data set properties are also categorised. • Some trends and shortcomings are identified in these methods and studies. [ABSTRACT FROM AUTHOR]

In the era of rapid paper publications in various venues, automatic citation recommendations would be highly useful to researchers when they write papers. Local citation recommendation aims to recommend possible papers to cite given local citation contexts. Previous work mainly computes the similarity score between citation contexts and cited papers on a one-to-one basis, which is quite time-consuming. We train a pair of neural network encoders that map citation contexts and all possible cited papers to the same vector space, respectively. After that, we index the positions of all cited papers in the vector space. This makes our process for searching recommended papers considerably faster. On the other hand, existing methods tend to recommend papers that are highly similar to each other, which makes recommendations lack diversity. Therefore, we extend our algorithm to perform multi-topic recommendations. We generate multi-topic training examples based on the index we mentioned earlier. Furthermore, we specially design a multi-group contrastive learning method to train our model so that it can distinguish different topics. Empirical experiments show that our model outperforms previous methods by a wide margin. Our model is also light weighted and has been deployed online so that researchers can use it to obtain recommended citations for their own paper in real-time. • Proposed FLCR algorithm with sentence-transformer & k-d tree. • Introduced multi-topic citation recommendation for diverse contexts. • Developed large-scale dataset with 1.7 million citation contexts for evaluation. • Demonstrated significant performance improvement over previous methods. • Deployed demo for real-time citation recommendations. [ABSTRACT FROM AUTHOR]

Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The overall goal of the FTC is to accommodate defects in the system components while they are in use and maintain stability with little to no performance reduction. These systems are crucial for mission-critical and safety-related applications where the safety of people is at stake and service continuity is crucial. In this review paper, a systematic study has been done for the development of FTC with machine learning, deep learning, and transfer learning algorithms. The challenges and limitations faced with their possible solutions through machine learning theories for the IFTC model are lined up. This paper guides researchers on the different possible types of machine learning algorithms and their advanced forms like deep learning and transfer learning. The differences among these are highlighted by the challenges and limitations of each. The paper is significant such that most of the important literature references from the Scopus database particularly related to important electrical and mechanical industrial problems have been discussed to guide the researchers who want to apply IFTC for specific industrial problems, being the research gap. Finally, future research directions for the development of IFTC are highlighted. [ABSTRACT FROM AUTHOR]

Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains. Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used. Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies. The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research. [Display omitted] • Application of sensors and artificial intelligence to analyse ankle biomechanics. • Lab sensors predict parameters, paving the way for post-modern rehabilitation. • Field devices show potential for ankle injury prediction. • Focus on streamlined smart sensors and effective data management in the future. [ABSTRACT FROM AUTHOR]

In this paper, to investigate the problem of radio wave propagation in complex environments, a new algorithm is proposed based on the equivalence principle algorithm and the domain decomposition method to rapidly and accurately predict electromagnetic scattering and radio wave propagation in half-space. First, equivalent surfaces are introduced to decompose the obstacle region and replace the scattering effect of obstacles within the equivalent surfaces with equivalent input and output electromagnetic currents. Then, the operator S, representing the relationship between the input and output equivalent electromagnetic currents on the equivalent surfaces, and the operator T, representing the interactions between the equivalent electromagnetic currents on each equivalent surface, are derived; this allows the calculation process of the overall spatial field in a complex environment to be simplified to the iterative calculation process of operators S and T. To reduce the complexity of calculating S, a neural network embedded with physical information is introduced to replace the calculation process. Finally, the training results are applied to an example and compared with the theoretical numerical results yielded by the method of moments in different cases with obstacles. The results reveal that the new method proposed in this paper is highly accurate and achieves a substantial improvement in calculation speed. Therefore, our proposed algorithm provides a new and reliable method for calculating the spatial field in complex environments. • The EPA is introduced to use equivalent surfaces to replace the scattering inside it. • The operators S and T are introduced into the process of solving the field. • A neural network with physical information embedded is applied to calculate the S. • Random undulating terrains are generated by Karhunen–Loève expansion. • MoM providing a good foundation for training is used to generate dataset. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a distributed machine learning method to protect users' privacy and security. It currently faces the following two problems (McMahan et al., 2017): (1) The performance of the global model degrades when dealing with Non-Independent Identically Distributed (Non-IID) data. Other existing classical methods do not have a rigorous theory based on error processing ; (2) In the process of global model aggregation, the classical FL algorithm either directly averages local models or solely considers the proportion of local data to assign weights to the models, without accounting for the discrepancies between the local models. In this paper, a federated aggregation algorithm Federated Maximum Mean Discrepancy (FedMMD) is proposed to address the deviation between the local models and Non-IID. First of all, this paper utilizes the Dilated Convolution Meet Transformer (DCMT) model for local model feature extraction. This approach aims to capture more feature information and minimize the impact of Non-IID scenarios. Secondly, the learning stability of the local model data participating in the aggregation is compared with Maximum Mean Discrepancy (MMD). The weights of the local models involved in the aggregation are determined using the SKNQ (Student–Keuls–Newman-Q) method and the entropy weight method. The SKNQ method calculates and compares the MMD across multiple clients, while the entropy weight method assigns different weights to clients based on their deviations. On the standard dataset, the FedAvg algorithm (McMahan et al., 2017), the FedProx algorithm (Li et al., 2020) and the FedMMD algorithm are compared. The experimental results demonstrate that the FedMMD algorithm, used in training the global model, not only enhances the accuracy of learning IID and Non-IID data but also improves the generalization capability of the global model. • An DCMT model is proposed to alleviate the model fluctuations caused by NonIID data. • An MMD method is proposed to compared horizontally before the model is aggregated. • Entropy weight method and the SKNQ method are combined to allocate local models' weight when they are fused into global model. [ABSTRACT FROM AUTHOR]

Accurate prediction of traffic flow is crucial to building a smart city. Given the nonlinearity of traffic flow, this paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence combined with the DROP strategy. The algorithm is referred to as TRELM-DROP. The Tent chaos strategy and residual correction method reduce the impact of randomness in traffic flow. On this basis, the Tent and residual correction strategy avoids the weight optimization of the ELM algorithm using the iterative method. A DROP strategy is proposed in the proposed algorithm to improve its ability to predict traffic flow under varying conditions. A comprehensive comparison of 36 real-world datasets is presented in this paper, comparing TRELM-DROP with other benchmark models. The results show that the proposed algorithm can produce the best prediction performance regarding various prediction error metrics under various traffic conditions without iterative optimization. • An improved non-iterative traffic flow forecasting model is proposed. • Tent chaos strategy and residual correction strategy are developed. • A DROP strategy combining Dropout and Dropconnect is proposed. [ABSTRACT FROM AUTHOR]

The paper focuses on the problem of instance selection. Instance selection is currently crucial to enhance the efficacy and efficiency of machine-learning tools when they are used to solve a data-mining task and when the data are large and they are seen through the prism of the big data phenomenon. Instance selection eliminates redundant instances and thus reduces the size of the training data set. The training data, with redundant cases removed, can be more useful and ensure better performance of the final classification models. The instance selection problem belongs to the NP-hard class, so it can be solved with an approximation tool. In this paper the firefly algorithm is proposed for solving the instance selection problem. This paper is one paper, where the firefly algorithm has been used to solve a discrete optimisation problem, when in more cases previously it has been used for solving continuous optimisation problems. The firefly-based instance selection algorithm is presented and its validation is carried out. The results of the computational experiment show that the algorithm is competitive with others. The results obtained are discussed and conclusions are formulated. [ABSTRACT FROM AUTHOR]

The paper presents a description of the research methodology dedicated to a two-step discretisation process applied to the input numeric data, with combining the characteristics of selected supervised and unsupervised algorithms, which leads to extended processing of some attributes in train and test sets. The methodology was illustrated with the investigations carried out in the domain of stylometric analysis of texts, for two datasets prepared for the task of binary authorship attribution. The several variants of transformed input data obtained were subjected to exploration using two selected machine learning methods capable of inducing knowledge from both continuous and categorical forms, namely the PART and J48 classifiers. The results from the experiments indicate that, as can be expected, supervised transformations of data work well enough, however, they do not always return the best outcome. The two-step processing of some attributes shows sufficient promise to warrant a closer study, as opposed to always unconditionally relying only on supervised algorithms as outperforming all other approaches. [ABSTRACT FROM AUTHOR]

In a dynamic world, data streams are continuously generated, which poses immense challenges for machine learning (ML) algorithms to adapt to changing statistical properties that are subject to a non-stationary context. The underlying scenario is defined as concept drift (CD), where changes in the relationship between response and prediction variables (real CD) or a change in input data (virtual CD) are accompanied by a significant degradation in the predictive performance of the models, causing ML models to reach unacceptable levels of system accuracy. In this paper, the state of the art for CD algorithms is analyzed and compared. For this purpose, a systematic literature review was performed. Then, the 10 most popular CD algorithms were extracted from the literature using a newly-developed metric. Subsequently, the algorithms were analyzed and compared with respect to their functionality and limitations. Based on these, the optimization potentials were systematically derived. This work presents a summarized overview of CD algorithms and provides the basis for algorithm optimization in this domain. [ABSTRACT FROM AUTHOR]

In this paper, the problem of imbalanced data stream classification is considered. To eliminate the problem of imbalanced data, over- and under-sampling procedures are implemented within a dedicated framework called Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI). To increase the quality of synthetic instance generation (carried out under the umbrella of an over-sampling procedure), an agent-based population algorithm is proposed. The problem is defined, and the agent-based algorithm is presented. A discussion of selected computational results is also included. [ABSTRACT FROM AUTHOR]

The development of quantum machine learning (QML) has received a lot of interest recently thanks to developments in both quantum computing (QC) and machine learning (ML). One of the ML paradigms that can be utilized to address challenging sequential decision-making issues is reinforcement learning (RL). It has been demonstrated that classical RL can successfully complete many difficult tasks. A leading method of building quantum RL agents relies on the variational quantum circuits (VQC). However, training QRL algorithms with VQCs requires significant amount of computational resources. This issue hurdles the exploration of various QRL applications. In this paper, we approach this challenge through asynchronous training of QRL agents. Specifically, we choose the asynchronous training of advantage actor-critic variational quantum policies. We demonstrate the results via numerical simulations that within the tasks considered, the asynchronous training of QRL agents can reach performance comparable to or superior to classical agents with similar model sizes and architectures. [ABSTRACT FROM AUTHOR]

Analyzing the runtime behaviors of Android apps is crucial for malware detection. In this paper, we attempt to learn the behavior level features of an app from function calls. The challenges of this task are twofold. First, the absence of function attributes hinders the understanding of app behaviors. Second, the graphical representation of function calls cannot be directly processed by classical machine learning algorithms. In this paper, we develop two methods to overcome these challenges. Based on function embedding, we first propose the concept of enhanced function call graphs (E-FCGs) to characterize app runtime behaviors. We then develop a Graph Convolutional Network (GCN) based algorithm to obtain vector representations of E-FCGs. Extensive experiments show that the features learned by our method can achieve surprisingly high detection performance on a variety of classifiers (e.g., LR, DT, SVM, KNN, RF, MLP and CNN), significantly outperforming the traditional static features. [ABSTRACT FROM AUTHOR]

In the 5G smart grid, battery liquid leakage negatively affects the safe storage and transportation of electricity. Image edge detection help locate the liquid leakage phenomenon. This paper proposes a digitalization-based algorithm that converts the image to the codeword and judges the edge by the code weight. Its main steps are image sampling, quantization, coding and judging. Compared with traditional Sobel and Canny operators, the algorithm dramatically enhances detection ability with machine learning. Moreover, this paper applies the algorithm to detect the leakage of infrared battery images in the 5G smart grid; The results show that it can accurately extract the liquid leakage location, improving fault diagnosis and early risk warning. [ABSTRACT FROM AUTHOR]

This paper presents a new training algorithm that can update the situation of layers' network, and therefore, connections, neurons, and firing rate of neurons based on FORCE (first-order reduced and controlled error) training algorithm. The Quantized Full FORCE algorithm (QFF) also updates the number of neurons and connections between different layers in the network per iteration in a way that the whole firing rate of each layer is updated via selecting the best neurons and combining strong features. The update method is sequential, so that with each instance passing through the network, the network structure is updated with the Full FORCE algorithm. The algorithm updates the structure of networks with a multiple/single middle layer of the supervised version of feed forward networks such as Multilayer perceptron (MLP), changing them into partially-connected networks. A combination of principal component analysis PCA and Linear Discriminant Analysis (LDA) algorithms has been used to cluster the network input features. The paper focuses on the deep supervised MLP network with backpropagation (BP) and various datasets and its comparison with other MLP based stat of art methods and hybrid evolutionary algorithms. We achieved 98.15 percent accuracy for facial expression 98.6 and 97.7 percent for Wisconsin breast Cancer and Iris Flower in respectively. The training algorithm employed in the study enjoys a lower computational complexity while yielding faster and more accurate convergence, starting with a very low level of errors of 0.009 in comparison with the full connection network and it solves the challenge of getting stuck in local minima and poor convergence of Gradient Decent with BP. • Providing the new training algorithm to update the connections and firing rate of neurons in each iteration. • The proposed training model is based on Quantized Full FORCE (first-order reduced and controlled error) training. • Providing an accurate framework to update the number of neurons, converting the network to a partial connection network. • In the new training model, the best features have the highest number of connections and neurons within the middle layers. • Being the decision speed of the network in the proposed method much faster than a fully connected network. • Cost-effectiveness and much lower resource requirements for implementation of deep networks on hardware such as FPGAs. [ABSTRACT FROM AUTHOR]

• Basic factors affecting deep learning models for streamflow forecasting. • Hybrid deep learning models for streamflow forecasting. • Input and hyperparameter optimization of deep learning models. • Limitation and prospects in streamflow forecasting. Deep learning has emerged as a powerful tool for streamflow forecasting and its applications have garnered significant interest in the hydrological community. Despite the publication of several review articles on machine learning applications in streamflow forecasting, no review paper has yet focused explicitly on deep learning and its hybrid forms. This paper starts with some characteristics of deep learning models to provide a quick view of deep learning. Next, the configurations and characteristics of hybrid deep learning models, which is a hybridization of modeling techniques with deep learning, are discussed. Another vital role while implementing deep learning modeling is the methods applied for input and hyperparameter optimization. Finally, the limitations encountered in streamflow forecasting using deep learning models and recommendations for further research are outlined. This review covers related studies from 2017 to 2023 to provide the most recent snapshot of deep learning modeling applications in streamflow forecasting. These efforts are expected to contribute to the advancement of streamflow forecasting, potentially enabling more informed decision-making in water resource management. [ABSTRACT FROM AUTHOR]

• The AES power system intelligent energy scheduling environment is built. • The BI-LSTM-Att action network is propose by combining BI-LSTM with attention mechanism. • The DQN-CE algorithm is proposed by improving the DQN algorithm. • The experiments on an all-electric ferry is conducted using a variety of methods. At present, Ship energy scheduling based on deep reinforcement learning (DRL) is an important research direction in which Deep Q learning algorithm (DQN) has been successfully applied in the field of energy scheduling. In terms of the shortcomings of DQN in all electric ships(AES) energy scheduling, such that, the insufficient performance of using multiplayer perceptron(MLP) as action network and the degradation of convergence performance due to over estimation of Q value, in this paper, we propose a DQN cross entropy(DQN-CE) energy scheduling algorithm combing bi-directional LSTM and attention mechanism(BI-LSTM-Att). Instead of using MLP, firstly, we use BI-LSTM-Att as action network of DQN, and then an improved DRL algorithm called DQN-CE is proposed, that is, we add cross-entropy loss of predicted action of the next time for the target network and the action network to DQN. Finally, we conduct simulation experiments on an all-electric ferry, and the experimental results show that the energy scheduling algorithm proposed in this paper reduces the economic consumption by 4.11%, increases the utilization rate of energy storage system by 24.4%, and reduces the exploration time of agent training by 31.3% compared with the original DQN energy scheduling algorithm. Finally, the effectiveness and superiority of the algorithm were further verified in the new case study. [ABSTRACT FROM AUTHOR]

The peak shear strength of clayey soil-geomembrane interfaces is a vital parameter for the design of relevant engineering infrastructure. However, due to the large number of influence factors and the complex action mechanism, accurate prediction of the peak shear strength for clayey soil-geomembrane interfaces is always a challenge. In this paper, a machine learning model was established by combining Mind Evolutionary Algorithm (MEA) and the ensemble algorithm of Adaptive Boosting Algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the peak shear strength of clayey soil-geomembrane interfaces based on the results of 623 laboratory interface direct shear experiments. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) tuned ADA-BPANN, MEA tuned Support Vector Machine (SVM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated, with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. By adopting the proposed novel model, sensitivity analysis was carried out, which indicates that normal pressure has the largest influence on the peak shear strength, followed by geomembrane roughness. Furthermore, an analytical equation was proposed to assess the peak shear strength that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the peak shear strength of clayey soil-geomembrane interfaces, which can provide benefits for the design of relevant engineering applications. • The ensemble artificial intelligence algorithms in predicting the peak shear strength of interfaces is compared. • An analytical equation for estimating the peak shear strength is proposed. • Physical experiments are conducted to validate the effectiveness of the proposed analytical equation. [ABSTRACT FROM AUTHOR]

K-Nearest Neighbors (KNN) are very popular supervised machine learning algorithm which can be effectively used for both multi-class classification and regression utilizing multiple features of the dataset. It trains extremely fast, that is with time complexity of O(n). However, the time complexity for generating results with KNN is O(n3dk) (where k=number of neighbours, d=dimension of data, n=number of training points) and hence it becomes very difficult to work with larger dataset. Although there are parallelizing and distributed computing techniques available for speeding up KNN, it still have limitations on horizontal scalability, fault tolerance and incremental learning capabilities. To solve these issues, the paper presents a KNN inspired novel Supervised Machine Learning algorithm called Distributed Nearest Hash utilizing unique usage of hashmap and primary key clustering order with help of wide column store databases facilitating easy incremental near-realtime scalability to train unlimited datapoints with O(n) time complexity and also predict them in near realtime with O(1) time complexity considering n >> d. This can be readily integrated in any system that uses KNN as a base classifier or regressor. The experimental results shows that the proposed DNH model is 25% faster than state of the art distributed KNN algorithm. Integrating with fuzzy logic the algorithm can be used for several class classification. Using this could aid in building very powerful smart decision making systems with very little computational power. [ABSTRACT FROM AUTHOR]

Deep learning technologies have been rapidly developing recently. They have shown excellent performances in many fields. However, deep learning networks have weak adaptability to small sample sizes.Usually, deep learning networks require tens of thousands of samples to avoid overfitting. In contrast, the kernel method can deal with small sample sizes. In this paper, we use only dozens of samples to train a successful classifier. Deep multiple kernel learning (DMKL) has emerged in recent years. It combines the ideas of deep learning and multiple kernel learning. Unfortunately, the DMKL network with a fixed structure cannot adapt to data of different dimensions and sizes. Therefore, in this paper, we propose a novel depth-width-scaling multiple kernel learning (DWS-MKL) algorithm. It has the ability to adjust the architecture according to the input data. We optimize the estimation of leave-one-out error by using the span bound instead of the dual objective function. Finally, we choose a large number of data sets from the UCI benchmark data sets for classification tasks. We also evaluate DWS-MKL on the MNIST data set. The experimental results show that different frameworks have different performances on the same dataset. Our DWS-MKL algorithm obtains better classification results than the state-of-the-art kernel learning algorithms, as determined by a thorough comparison. The encouraging results demonstrate that our method has the potential to improve generalization performance of the model. [ABSTRACT FROM AUTHOR]

This paper investigates the fully complex mini-batch gradient algorithm for training complex-valued neural networks. Mini-batch gradient method has been widely used in neural network training, however, its convergence analysis is usually restricted to real-valued neural networks and of probability nature. By introducing a new Taylor mean value theorem for analytic functions, in this paper we establish deterministic convergence results for the fully complex mini-batch gradient algorithm under mild conditions. The deterministic convergence here means that the algorithm will deterministically converge, and both the weak convergence and strong convergence will be proved. Benefited from the newly introduced mean value theorem, our results are of global nature in that they are valid for arbitrarily given initial values of the weights. The theoretical findings are validated with a simulation example. [ABSTRACT FROM AUTHOR]

The amount of network traffic generated from the Internet of Things (IoT) devices is massive which leads to Big data. In present scenario, IoT devices has taken a leap ahead in terms of growing technology and insecure network traffic flow. The sensitivity of the data in the IoT network is becoming very high, which concern the security in legal and privacy issues. Traditional Intrusion Detection System (IDS) is used as a primary line security to differentiate between the attack and benign network traffic flow. As the size of the network traffic capture increases, it becomes more important to handle such big data. Similarly, when the number of classes and instances increases, the complexity of the data also increases. In this paper, we propose a big data platform based intrusion detection system which can differentiate between the types of network traffic flow generated from the IoT devices. We compared the performance of deep learning algorithms with machine learning algorithms on Apache Spark platform and found that machine learning algorithms outperform deep learning algorithms with greater accuracy and less training time for the model. We evaluated our research based on real world network traffic dataset BoT-IoT [1] for an improved intrusion detection system in IoT network traffic. [ABSTRACT FROM AUTHOR]