A data mining analysis of COVID-19 cases in states of United States of America

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.

Penggunaan Metode K-Means Cluster Untuk Mengklasifikasikan Kemampuan 4C Mahasiswa

The K-means method is a non-hierarchical belongs to an algorithmic technique for grouping items into K clusters by minimizing the SS sum of square (SS) distance to the centroid cluster. In the K-means method, the number of clusters can be determined by the researchers themselves. The K-means method can be applied to all fields, including education. In facing the 21st century, many students must equip themselves with many abilities, including critical thinking, collaboration, communication, and creativity and innovation, referred to as 4C abilities. This study aims to apply the K-means method to classify students' 4C abilities. The population in this study were students of the English Language State Institute of Islamic Studies Ponorogo class of 2017 and class of 2018. The sampling technique was carried out using the stratified random sampling method with a sample size of 71 divided into 30 students of the 2017 class and 41 students of the 2018 class. In identifying outliers, it is known that 16 students enter outlier detection not to be analyzed. The results of the classification using the K-means method showed that there were three groups with a composition of 20 students in the high group, 20 students in the medium group, and 15 students in the low group with an R2 value of 41.7%. Based on the test results of ANOVA, it is known that the three groups, based on their ability level, have differences. These results showed that each group formed had a noticeable difference, albeit with an R2 value that was not large.

Approximation Algorithms for the Bottleneck Asymmetric Traveling Salesman Problem

We present the first nontrivial approximation algorithm for the bottleneck asymmetric traveling salesman problem . Given an asymmetric metric cost between n vertices, the problem is to find a Hamiltonian cycle that minimizes its bottleneck (or maximum-length edge) cost. We achieve an O (log n / log log n ) approximation performance guarantee by giving a novel algorithmic technique to shortcut Eulerian circuits while bounding the lengths of the shortcuts needed. This allows us to build on a related result of Asadpour, Goemans, Mądry, Oveis Gharan, and Saberi to obtain this guarantee. Furthermore, we show how our technique yields stronger approximation bounds in some cases, such as the bounded orientable genus case studied by Oveis Gharan and Saberi. We also explore the possibility of further improvement upon our main result through a comparison to the symmetric counterpart of the problem.

The Power of Subsampling in Submodular Maximization

We propose subsampling as a unified algorithmic technique for submodular maximization in centralized and online settings. The idea is simple: independently sample elements from the ground set and use simple combinatorial techniques (such as greedy or local search) on these sampled elements. We show that this approach leads to optimal/state-of-the-art results despite being much simpler than existing methods. In the usual off-line setting, we present SampleGreedy, which obtains a [Formula: see text]-approximation for maximizing a submodular function subject to a p-extendible system using [Formula: see text] evaluation and feasibility queries, where k is the size of the largest feasible set. The approximation ratio improves to p + 1 and p for monotone submodular and linear objectives, respectively. In the streaming setting, we present Sample-Streaming, which obtains a [Formula: see text]-approximation for maximizing a submodular function subject to a p-matchoid using O(k) memory and [Formula: see text] evaluation and feasibility queries per element, and m is the number of matroids defining the p-matchoid. The approximation ratio improves to 4p for monotone submodular objectives. We empirically demonstrate the effectiveness of our algorithms on video summarization, location summarization, and movie recommendation tasks.

Neuromodulated Dopamine Plastic Networks for Heterogeneous Transfer Learning with Hebbian Principle

The plastic modifications in synaptic connectivity is primarily from changes triggered by neuromodulated dopamine signals. These activities are controlled by neuromodulation, which is itself under the control of the brain. The subjective brain’s self-modifying abilities play an essential role in learning and adaptation. The artificial neural networks with neuromodulated plasticity are used to implement transfer learning in the image classification domain. In particular, this has application in image detection, image segmentation, and transfer of learning parameters with significant results. This paper proposes a novel approach to enhance transfer learning accuracy in a heterogeneous source and target, using the neuromodulation of the Hebbian learning principle, called NDHTL (Neuromodulated Dopamine Hebbian Transfer Learning). Neuromodulation of plasticity offers a powerful new technique with applications in training neural networks implementing asymmetric backpropagation using Hebbian principles in transfer learning motivated CNNs (Convolutional neural networks). Biologically motivated concomitant learning, where connected brain cells activate positively, enhances the synaptic connection strength between the network neurons. Using the NDHTL algorithm, the percentage of change of the plasticity between the neurons of the CNN layer is directly managed by the dopamine signal’s value. The discriminative nature of transfer learning fits well with the technique. The learned model’s connection weights must adapt to unseen target datasets with the least cost and effort in transfer learning. Using distinctive learning principles such as dopamine Hebbian learning in transfer learning for asymmetric gradient weights update is a novel approach. The paper emphasizes the NDHTL algorithmic technique as synaptic plasticity controlled by dopamine signals in transfer learning to classify images using source-target datasets. The standard transfer learning using gradient backpropagation is a symmetric framework. Experimental results using CIFAR-10 and CIFAR-100 datasets show that the proposed NDHTL algorithm can enhance transfer learning efficiency compared to existing methods.

Projective toric codes

Any integral convex polytope [Formula: see text] in [Formula: see text] provides an [Formula: see text]-dimensional toric variety [Formula: see text] and an ample divisor [Formula: see text] on this variety. This paper gives an explicit construction of the algebraic geometric error-correcting code on [Formula: see text], obtained by evaluating global section of the line bundle corresponding to [Formula: see text] on every rational point of [Formula: see text]. This work presents an extension of toric codes analogous to the one of Reed–Muller codes into projective ones, by evaluating on the whole variety instead of considering only points with nonzero coordinates. The dimension of the code is given in terms of the number of integral points in the polytope [Formula: see text] and an algorithmic technique to get a lower bound on the minimum distance is described.

Optimization of Two-Stage IPD-(1+I) Controllers for Frequency Regulation of Sustainable Energy Based Hybrid Microgrid Network

Sustainable energy based hybrid microgrids are advantageous in meeting constantly increasing energy demands. Conversely, the intermittent nature of renewable sources represents the main challenge to achieving a reliable supply. Hence, load frequency regulation by adjusting the amount of power shared between subsystems is considered as a promising research field. Therefore, this paper presents a new stratagem for frequency regulation by developing a novel two stage integral-proportional-derivative with one plus integral (IPD-(1+I)) controller for multi sources islanded microgrid system (MS-IμGS). The proposed stratagem has been tested in an MS-IμGS comprising of a wind turbine, parabolic trough, biodiesel generators, solid-oxide fuel cell, and electric water heater. The proposed model under different scenarios is simulated in MATLAB environment considering the real-time recorded wind data. A recently developed sine-cosine algorithmic technique (SCA) has been leveraged for optimal regulation of frequency in the considered microgrid. To identify the supremacy of the proposed technique, comparative studies with other classical controllers with different optimization techniques have been performed. From the comparison, it is clearly evident that, SCA-(IPD-(1+I)) controller gives better performance over other considered stratagems in terms of various time domain specific parameters, such as peak deviations (overshoot, undershoot) and settling time. Finally, the robustness of the proposed stratagem is evaluated by conducting sensitivity analysis under ±30% parametric variations and +30% load demand. The lab tests results validate the operation of the proposed system and show that it can be used to regulate the frequency in stand-alone microgrids with a high penetration of renewable energy.

Performance Comparison for Spam Detection in Social Media Using Deep Learning Algorithms

Social media applications like Twitter, Instagram, Facebook have helped people to connect to each other. This has been eased due to high-speed internet. However, this has invited various spam messages through tweets or Facebook. The sole purpose of such messages is aggregation or exploitation of personal data in terms of finances or medical records, political benefit’s or community violence. This makes spam detection an extreme value-added service. We tend to recommend a 1D CNN algorithmic technique and compare results with variants of CNN and with boosting algorithms. The model is braced with linguistics data in the illustration of the words with the assistance of knowledge-bases such as Word2vec and fast ext. This improves the end to end performance, by providing higher linguistics vector illustration of input testing words. Projected Experimental results show the efficiency of the projected approach from the point of view of accuracy, F1-score and response time.

On the Descriptive Complexity of Color Coding

Color coding is an algorithmic technique used in parameterized complexity theory to detect “small” structures inside graphs. The idea is to derandomize algorithms that first randomly color a graph and then search for an easily-detectable, small color pattern. We transfer color coding to the world of descriptive complexity theory by characterizing—purely in terms of the syntactic structure of describing formulas—when the powerful second-order quantifiers representing a random coloring can be replaced by equivalent, simple first-order formulas. Building on this result, we identify syntactic properties of first-order quantifiers that can be eliminated from formulas describing parameterized problems. The result applies to many packing and embedding problems, but also to the long path problem. Together with a new result on the parameterized complexity of formula families involving only a fixed number of variables, we get that many problems lie in FPT just because of the way they are commonly described using logical formulas.