Search

In this paper, we study a discrete momentum consensus-based optimization (Momentum-CBO) algorithm which corresponds to a second-order generalization of the discrete first-order CBO [S.-Y. Ha, S. Jin and D. Kim, Convergence of a first-order consensus-based global optimization algorithm, Math. Models Methods Appl. Sci. 30 (2020) 2417–2444]. The proposed algorithm can be understood as the modification of ADAM-CBO, replacing the normalization term by unity. For the proposed Momentum-CBO, we provide a sufficient framework which guarantees the convergence of algorithm toward a global minimum of the objective function. Moreover, we present several experimental results showing that Momentum-CBO has an improved success rate of finding the global minimum compared to vanilla-CBO and show the stability of Momentum-CBO under different initialization schemes. We also show that Momentum-CBO can be used as the alternative of ADAM-CBO which does not have a proper convergence analysis. Finally, we give an application of Momentum-CBO for Lyapunov function approximation using symbolic regression techniques. [ABSTRACT FROM AUTHOR]

The clinical diagnosis of heart disease in most situations is based on a difficult amalgamation of pathological and clinical information. Because of this complication, there is a significant level of curiosity among many diagnostic healthcare professionals and researchers who are keenly interested in the efficient, accurate, and early-stage forecasting of heart disease. Deep Learning Algorithms aid in the prediction of heart disease. The main focus of this paper is to develop a method for predicting heart disease through Modified Rough K means + + (MRK + +) clustering along with the Restricted Boltzmann Machine (RBM). This paper is categorized into two modules: (1) Propose a clustering component based on Modified Rough K-means + + ; (2) disease prediction based on RBM. The input Cleveland dataset is clustered using the stochastic probabilistic rough k-means + + clustering technique in the module for clustering. The clustered data is acquired and used in the RBM, and this hybrid structure is then used in the heart disease forecasting module. Throughout the testing procedure, the most valid result is chosen from the clustered test data, and the RBM classifier that correlates to the nearest cluster in the test data is based on the smallest distance or similar parameters. Furthermore, the output value is used to predict heart disease. There are three different types of experiments that are performed: In the first experiment comprises modifying the rough K-means + + clustering algorithm, the second experiment evaluates the classification result, and the third experiment suggests hybrid model representation. When the Hybrid Modified Rough k-means + + - RBM model is compared with any single model, it provides the highest accuracy. [ABSTRACT FROM AUTHOR]

Tremendous efficient optimization methods have been proposed for strongly convex objectives optimization in modern machine learning. For non-strongly convex objectives, a popular approach is to apply a reduction from non-strongly convex to a strongly convex case via regularization techniques. Reduction on objectives with adaptive decrease on regularization tightens the optimal convergence of algorithms to be independent on logarithm factor. However, the initialization of parameter of regularization has a great impact on the performance of the reduction. In this paper, we propose an aggressive reduction to reduce the complexity of optimization for non-strongly convex objectives, and our reduction eliminates the impact of the initialization of parameter on the convergent performances of algorithms. Aggressive reduction not only adaptively decreases the regularization parameter, but also modifies regularization term as the distance between current point and the approximate minimizer. Our aggressive reduction can also shave off the non-optimal logarithm term theoretically, and make the convergent performance of algorithm more compact practically. Experimental results on logistic regression and image deblurring confirm this success in practice. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) has a wide application in daily life. With wearable sensors, people's activity can be monitored, recorded and analysed. However, most existing methods did not make full use of human activity data and their features. In this paper, a new method based on feature selection and clustering algorithm is proposed. We established two-layer classification models, respectively, in the ankle, the chest, the wrist and the mixed accelerometer data. K-means clustering algorithm was first used to obtain a broad classification of the activities and then we conducted two rounds of classification, among which feature selection was performed in each layer. A significance analysis was also carried out in the final experiment and we compared the performance of the final model from the mixed accelerometer data with other algorithms, the results showed that the recognition performance of our model was significantly better, and the average F 1 score was as high as 0.969 in publicly available PAMAP2 dataset. Compared with other methods, our model achieved the highest recognition rate. The method proposed in this paper can greatly improve the recognition rate of human activity and effectively evaluate daily activity. [ABSTRACT FROM AUTHOR]

In this paper, we discuss applications of neural networks to recognizing knots and, in particular, to the unknotting problem. One of the motivations for this study is to understand how neural networks work on the example of a problem for which rigorous mathematical algorithms for its solution are known. We represent knots by rectangular Dynnikov diagrams and apply neural networks to distinguish a given diagram's class from a finite family of topological types. The data presented to the program is generated by applying Dynnikov moves to initial samples. The significance of using these diagrams and moves is that in this context the problem of determining whether a diagram is unknotted is a finite search of a bounded combinatorial space. In this way, this paper provides a foundation for further work where the neural network itself will learn to use the Dynnikov moves for knot recognition. Source code of the programs is available at https://github.com/nerusskikh/deepknots. [ABSTRACT FROM AUTHOR]

GPS receivers have a wide range of applications, but are not always secure. A spoofing attack is one source of conscious errors in which the counterfeit signal overcomes the authentic GPS signal and takes control of the receiver's operation. Recently, GPS spoofing attack detection based on computational algorithms, such as machine learning, classification, wavelet transform and clustering, has been developing. This paper proposes multiple clustering algorithms for accurately clustering the authentic and spoofing signals, called subtractive, FCM and DBSCAN clustering. The spoofing attack is recognized using two distinct features: moving phase detector variance and norms of correlators. Spoofing and authentic signals have different patterns in the proposed features. According to the Dunn and Silhouette indexes, the validation of the results is investigated. The Dunn values for the proposed approaches are 0.8592, 0.5285 and 0.6039 for DBSCAN, FCM and subtractive clustering, respectively. Also, the DBSCAN algorithm is implemented at the RTL level because of its highest value for the Dunn index and algorithm verifiability. Using the Vivado tools, this algorithm is implemented and designed on a Xilinx Virtex 7 xc7vx690tffg1930-3 hardware device for two-dimensional data with 32-bit accuracy and 130 data points. [ABSTRACT FROM AUTHOR]

In the last years, the number of machine learning algorithms and their parameters has increased significantly. On the one hand, this increases the chances of finding better models. On the other hand, it increases the complexity of the task of training a model, as the search space expands significantly. As the size of datasets also grows, traditional approaches based on extensive search start to become prohibitively expensive in terms of computational resources and time, especially in data streaming scenarios. This paper describes an approach based on meta-learning that tackles two main challenges. The first is to predict key performance indicators of machine learning models. The second is to recommend the best algorithm/configuration for training a model for a given machine learning problem. When compared to a state-of-the-art method (AutoML), the proposed approach is up to 130x faster and only 4% worse in terms of average model quality. Hence, it is especially suited for scenarios in which models need to be updated regularly, such as in streaming scenarios with big data, in which some accuracy can be traded for a much shorter model training time. [ABSTRACT FROM AUTHOR]

In this paper, we study kernel ridge-less regression, including the case of interpolating solutions. We prove that maximizing the leave-one-out ( CV loo ) stability minimizes the expected error. Further, we also prove that the minimum norm solution — to which gradient algorithms are known to converge — is the most stable solution. More precisely, we show that the minimum norm interpolating solution minimizes a bound on CV loo stability, which in turn is controlled by the smallest singular value, hence the condition number, of the empirical kernel matrix. These quantities can be characterized in the asymptotic regime where both the dimension (d) and cardinality (n) of the data go to infinity (with n d → γ as d , n → ∞). Our results suggest that the property of CV loo stability of the learning algorithm with respect to perturbations of the training set may provide a more general framework than the classical theory of Empirical Risk Minimization (ERM). While ERM was developed to deal with the classical regime in which the architecture of the learning network is fixed and n → ∞ , the modern regime focuses on interpolating regressors and overparameterized models, when both d and n go to infinity. Since the stability framework is known to be equivalent to the classical theory in the classical regime, our results here suggest that it may be interesting to extend it beyond kernel regression to other overparameterized algorithms such as deep networks. [ABSTRACT FROM AUTHOR]

To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms. [ABSTRACT FROM AUTHOR]

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models. [ABSTRACT FROM AUTHOR]

There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm++, BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies. [ABSTRACT FROM AUTHOR]

With the continuous improvement of science and technology, the substation remote control system has been constantly improved, which provides the possibility for the complete realization of intelligent and unmanned substation. However, due to the special substation environment, it is easy to cause interference, coupled with the low accuracy of today's video image processing algorithm, which leads to the frequent occurrence of false alarms and missing alarms. Manual intervention is needed to deal with this, which inhibits the display of automatic intelligent substation processing functions. Therefore, in this paper, the most rapidly developed machine learning algorithm — deep learning is applied to the substation instrument equipment identification processing, in order to improve the accuracy and efficiency of instrument equipment identification, and make due contributions to the full realization of unattended substation. [ABSTRACT FROM AUTHOR]

This paper describes a novel call recognizer system based on the machine learning approach. Current trends, intelligence, emotional recognition and other factors are important challenges in the real world. The proposed system provides robustness with high accuracy and adequate response time for the human–computer interaction. Intelligence and emotion recognition from the speech of human–computer interfaces are simulated via multiple classifier systems (MCSs). At a higher-level stage, the acoustic stream phase extracts certain acoustic features based on the pitch and energy of the signal. Here, the feature space is labeled with various emotional types in the training phase. Emotional categories are trained in the acoustic feature space. The semantic stream process converts speech into text in the input speech signal. Text classification algorithms are applied subsequently. The clustering and classification process is performed via a K -means algorithm. The detection of the Tone of Voice of call recognition system is achieved with the XGBoost model for feature extraction and detection of a particular phrase in the client call phase. Speech expressions are used for understanding the human emotion. The algorithms are tested and demonstrate good performance in the simulation environment. [ABSTRACT FROM AUTHOR]