Neutrosophic Exponential Distribution: Modeling and Applications for Complex Data Analysis

The exponential distribution has always been prominent in various disciplines because of its wide range of applications. In this work, a generalization of the classical exponential distribution under a neutrosophic environment is scarcely presented. The mathematical properties of the neutrosophic exponential model are described in detail. The estimation of a neutrosophic parameter by the method of maximum likelihood is discussed and illustrated with examples. The suggested neutrosophic exponential distribution (NED) model involves the interval time it takes for certain particular events to occur. Thus, the proposed model may be the most widely used statistical distribution for the reliability problems. For conceptual understanding, a wide range of applications of the NED in reliability engineering is given, which indicates the circumstances under which the distribution is suitable. Furthermore, a simulation study has been conducted to assess the performance of the estimated neutrosophic parameter. Simulated results show that imprecise data with a larger sample size efficiently estimate the unknown neutrosophic parameter. Finally, a complex dataset on remission periods of cancer patients has been analyzed to identify the importance of the proposed model for real-world case studies.

Hope for the future: An analysis of HOPE/HÅP(E) across genres and languages

This article reports on a contrastive study of the cognate nouns and verbs hope and håp(e) that investigates their lexico-grammatical conditions of use in English vs. Norwegian fiction texts and football match reports. The complex dataset consists of material from a parallel corpus of fiction texts and a comparable corpus of football match reports. An interesting finding is that the verb use outnumbers the noun use in the fiction texts, whereas the noun use outnumbers the verb use in the match reports in both languages. Moreover, the analysis of the lemmas suggests that they have similar potential of use but with slightly different preferences, both across the genres and languages. It is also suggested that the English lemmas are more consistently used in negative contexts than the Norwegian ones. Finally, the method of combining data from two different types of contrastive corpora proved fruitful, as the results become more robust.

Geodatabase of Publicly Available Information about Czech Municipalities’ Local Administration

In this data description, we introduce a unique (geo)dataset with publicly available information about the municipalities focused on (geo)participatory aspects of local administration. The dataset comprises 6258 Czech municipalities linked with their respective administrative boundaries. In total, 55 attributes were prepared for each municipality. We also describe the process of data collection, processing, verification, and publication as open data. The uniqueness of the dataset is that such a complex dataset regarding geographical coverage with a high level of detail (municipalities) has never been collected in Czechia before. Besides, it could be applied in various research agendas in public participation and local administration and used thematically using selected indicators from various participation domains. The dataset is available freely in the Esri geodatabase, geospatial services using API (REST, GeoJSON), and other common non-spatial formats (MS Excel and CSV).

Research on Generation Method of Grasp Strategy Based on DeepLab V3+ for Three-Finger Gripper

A reasonable grasping strategy is a prerequisite for the successful grasping of a target, and it is also a basic condition for the wide application of robots. Presently, mainstream grippers on the market are divided into two-finger grippers and three-finger grippers. According to human grasping experience, the stability of three-finger grippers is much better than that of two-finger grippers. Therefore, this paper’s focus is on the three-finger grasping strategy generation method based on the DeepLab V3+ algorithm. DeepLab V3+ uses the atrous convolution kernel and the atrous spatial pyramid pooling (ASPP) architecture based on atrous convolution. The atrous convolution kernel can adjust the field-of-view of the filter layer by changing the convolution rate. In addition, ASPP can effectively capture multi-scale information, based on the parallel connection of multiple convolution rates of atrous convolutional layers, so that the model performs better on multi-scale objects. The article innovatively uses the DeepLab V3+ algorithm to generate the grasp strategy of a target and optimizes the atrous convolution parameter values of ASPP. This study used the Cornell Grasp dataset to train and verify the model. At the same time, a smaller and more complex dataset of 60 was produced according to the actual situation. Upon testing, good experimental results were obtained.

Accelerating Convolutional Neural Network Using Discrete Orthogonal Transforms

<div> <div> <div> <div> <p>All experiments are implemented in Python, using the PyTorch and the Torch-DCT libraries under the Google Colab environment. The Intel(R) Xeon(R) CPU @ 2.00GHz and a Tesla V100-SXM2-16GB GPU were assignment to the Google Colab runtime when profiling the DOT models. It should be noted that the current stable version of the PyTorch library, version 1.8.1, offers only the implementation of the FFT algorithm. Therefore, the implementations of the Hartley and Cosine transforms, listed in Table 1, are not implemented using the same optimizations (algorithm and code wise) adopted in the FFT. We benchmark the DOT methods using the LENET-5 network shown in Figure 10. The ReLU activation function is adopted a non-linear operation across the entire architecture. In this network, the convolutional operations have a kernel of size K = 5. The convolution is of type “valid”, i.e., padding is not applied to the input. Hence the output size M of each layer is smaller than its input size N, that is M=N−K+1. The optimizers used in our experiments are Adam, SGD, SGD with Momentum of 0.9, and RMSProp with α = 0.99. The StepLR scheduler is used with a step size of 20 epochs and a γ = 0.5. We train our model for 40 epochs using a mini-batch of size 128 and a learning rate of 0.001. Five datasets are used in order to benchmark the proposed DOT methods. Among them, we have the MNIST dataset and some variants of the MNIST dataset such as EMNIST, KMNIST and Fashion-MNIST. Additionally, a more complex dataset, CIFAR-10 is also used in our benchmark.</p> </div> </div> </div> </div>

Accelerating Convolutional Neural Network Using Discrete Orthogonal Transforms

<div> <div> <div> <div> <p>All experiments are implemented in Python, using the PyTorch and the Torch-DCT libraries under the Google Colab environment. The Intel(R) Xeon(R) CPU @ 2.00GHz and a Tesla V100-SXM2-16GB GPU were assignment to the Google Colab runtime when profiling the DOT models. It should be noted that the current stable version of the PyTorch library, version 1.8.1, offers only the implementation of the FFT algorithm. Therefore, the implementations of the Hartley and Cosine transforms, listed in Table 1, are not implemented using the same optimizations (algorithm and code wise) adopted in the FFT. We benchmark the DOT methods using the LENET-5 network shown in Figure 10. The ReLU activation function is adopted a non-linear operation across the entire architecture. In this network, the convolutional operations have a kernel of size K = 5. The convolution is of type “valid”, i.e., padding is not applied to the input. Hence the output size M of each layer is smaller than its input size N, that is M=N−K+1. The optimizers used in our experiments are Adam, SGD, SGD with Momentum of 0.9, and RMSProp with α = 0.99. The StepLR scheduler is used with a step size of 20 epochs and a γ = 0.5. We train our model for 40 epochs using a mini-batch of size 128 and a learning rate of 0.001. Five datasets are used in order to benchmark the proposed DOT methods. Among them, we have the MNIST dataset and some variants of the MNIST dataset such as EMNIST, KMNIST and Fashion-MNIST. Additionally, a more complex dataset, CIFAR-10 is also used in our benchmark.</p> </div> </div> </div> </div>

Scalable Data Processing for Prediction, Batch Computation and Analysis and Response Times using Google BigQuery

Computing a complex dataset analysis is tedious task because distributed resource management is difficult task. Google disperses the registering utilized by BigQuery across process assets powerfully which implies that we don't need to oversee figure asset, for example, bunches, register motor, stockpiling structure. Fighting commitments customarily require custom estimating (and esteeming) of unequivocal procedure gatherings, and this can change after some time which can be trying. Since Google logically assigns resources, costs are dynamic too. Google offers both a compensation all the more just as costs emerge elective where you pay for the data brought into BigQuery and subsequently per question costs. Since BigQuery is a totally managed organization, the backend game plan and tuning is managed by Google. This is much more direct than battling plans that anticipate that you should pick a number and sort of gatherings to make and to administer after some time. BigQuery consequently recreates information between zones to empower high accessibility. It additionally naturally load adjusts to give ideal execution and to limit the effect of any equipment disappointments. So getting benefits of BigQuery we did complex data analysis in huge amount of data set within a friction of second. Our result is showing the capability of our research work in the field of scalable data processing.

An Improved Gravitational Clustering Based on Local Density

Gravitational clustering algorithm (Gravc) is a novel and excellent dynamic clustering algorithm that can accurately cluster complex dataset with arbitrary shape and distribution. However, high time complexity is a key challenge to the gravitational clustering algorithm. To solve this problem, an improved gravitational clustering algorithm based on the local density is proposed in this paper, called FastGravc. The main contributions of this paper are as follows. First of all, a local density-based data compression strategy is designed to reduce the number of data objects and the number of neighbors of each object participating in the gravitational clustering algorithm. Secondly, the traditional gravity model is optimized to adapt to the quality differences of different objects caused by data compression strategy. And then, the improved gravitational clustering algorithm FastGravc is proposed by integrating the above optimization strategies. Finally, extensive experimental results on synthetic and real-world datasets verify the effectiveness and efficiency of FastGravc algorithm.

Automatic Development of Deep Learning Architectures for Image Segmentation

Machine learning is a branch of artificial intelligence that has gained a lot of traction in the last years due to advances in deep neural networks. These algorithms can be used to process large quantities of data, which would be impossible to handle manually. Often, the algorithms and methods needed for solving these tasks are problem dependent. We propose an automatic method for creating new convolutional neural network architectures which are specifically designed to solve a given problem. We describe our method in detail and we explain its reduced carbon footprint, computation time and cost compared to a manual approach. Our method uses a rewarding mechanism for creating networks with good performance and so gradually improves its architecture proposals. The application for the algorithm that we chose for this paper is segmentation of eyeglasses from images, but our method is applicable, to a larger or lesser extent, to any image processing task. We present and discuss our results, including the architecture that obtained 0.9683 intersection-over-union (IOU) score on our most complex dataset.

Voting Classification Method for Network Traffic Prediction

The prediction analysis is the approach of data mining which is applied to predict future possibilities based on the current information. The network traffic classification is the major issue of the prediction analysis due to complex dataset. The network traffic techniques have three steps, which are preprocessing, feature extraction and classification. In the phase of pre-processing data set is collected which is processed to removed missing and redundant values. In the second phase, the relationship between attribute and target set is established. In the last phase, the technique of classification is applied for the classification. This research study has been influenced by the different intrusion threats on internet and the ways to detect them. In this research, we have studied and analyzed the famous network traffic data -NSL KDD dataset and its various features. The proposed model is a hybrid of Logistic Regression and Knearest neighbor classifier combined using voting classifier, which aims at classifying the data into malicious and nonmalicious with more accuracy than existing methods.