Automatic Color Extraction Algorithm of Graphic Design Image Based on Artificial Intelligence

The extraction of color features plays an important role in image recognition and image retrieval. In the past, feature extraction mainly depends on manual or supervised learning, which limits the automation of the whole recognition or retrieval process. In order to solve the above problems, an automatic color extraction algorithm based on artificial intelligence is proposed. According to the characteristics of BMP image, the paper makes use of the conversion between image color space and realizes it in the visual C++6.0 environment. The experimental results show that the algorithm realizes the basic operation of image preprocessing, and realizes the automatic extraction of image color features by proper data clustering algorithm.

Differentially Private Set Union

We study the basic operation of set union in the global model of differential privacy. In this problem, we are given a universe $U$ of items, possibly of infinite size, and a database $D$ of users. Each user $i$ contributes a subset $W_i \subseteq U$ of items. We want an ($\epsilon$,$\delta$)-differentially private algorithm which outputs a subset $S \subset \cup_i W_i$ such that the size of $S$ is as large as possible. The problem arises in countless real world applications; it is particularly ubiquitous in natural language processing (NLP) applications as vocabulary extraction. For example, discovering words, sentences, $n$-grams etc., from private text data belonging to users is an instance of the set union problem.Known algorithms for this problem proceed by collecting a subset of items from each user, taking the union of such subsets, and disclosing the items whose noisy counts fall above a certain threshold. Crucially, in the above process, the contribution of each individual user is always independent of the items held by other users, resulting in a wasteful aggregation process, where some item counts happen to be way above the threshold. We deviate from the above paradigm by allowing users to contribute their items in a {\em dependent fashion}, guided by a {\em policy}. In this new setting ensuring privacy is significantly delicate. We prove that any policy which has certain {\em contractive} properties would result in a differentially private algorithm. We design two new algorithms for differentially private set union, one using Laplace noise and other Gaussian noise, which use $\ell_1$-contractive and $\ell_2$-contractive policies respectively and provide concrete examples of such policies. Our experiments show that the new algorithms in combination with our policies significantly outperform previously known mechanisms for the problem.

Data Envelopment Analysis Algorithm of Enterprise Economic Benefits Based on Leapfrog Algorithm

In order to deal with the problems of low analysis accuracy and poor security in the current enterprise economic benefit data analysis, this paper proposes an enterprise economic benefit data envelopment analysis algorithm based on leapfrog algorithm. According to the proposed method, with the help of comparing and analyzing different analysis algorithms of enterprise economic benefit data, the data envelopment analysis algorithm with strong usability is selected firstly. After that, the effectiveness of DEA is judged by introducing relaxation variables with the help of the data envelopment analysis algorithm, and the non-Archimedean infinitesimal model is used to build the basic operation mechanism. On this basis, the overall operation mechanism of leapfrog algorithm component is used to clarify the value range of relevant economic benefit data and the number of global mixed iterations. The experimental results show that the envelope analysis of enterprise economic benefit-related data using this method can not only improve the accuracy of data analysis but also effectively improve the security of relevant business data.

Modeling of U-shaped composite detectors

We have investigated the basic operation of a composite detector comprising of elements arranged in the shape of an U-shaped rectangular well. Considering an isotropic scattering of gamma-rays and partial energy absorptions in up to four detector modules, expressions for the addback factor and the peak-to-total ratio have been obtained in terms of only one probability amplitude. We have compared the performance of two U-shaped detectors having different geometries and observed negligible gain in addback due to the longer arms. For completeness, comparisons have been made with composite detectors like the two element stacked detector and the two level pyramidal detector, both being embedded inside the U-shaped detector. Our pen-on-paper approach could be used to understand the operation of modern arrays having detector elements arranged in various sophisticated ways.

A recurrent circuit links antagonistic cerebellar modules during associative motor learning

The neural architecture of the cerebellum is thought to be specialized for performing supervised learning: specific error-related climbing fiber inputs are used to teach sensorimotor associations to small ensembles of Purkinje cells located in functionally distinct modules that operate independently of each other in a purely feedforward manner. Here, we test whether the basic operation of the cerebellum complies with this basic architecture in mice that learned a simple sensorimotor association during eyeblink conditioning. By recording Purkinje cells in different modules and testing whether their responses rely on recurrent circuits, our results reveal three operational principles about the functional organization of the cerebellum that stand in stark contrast to the conventional view: (1) Antagonistic organization, (2) Recurrent network dynamics, and (3) Intermodular communication. We propose that the neural architecture of the cerebellum implements these three operational principles to achieve optimal performance and solve a number of problems in motor control.

Hazardous area classification for wheat flour processing plant

Dust explosion possibly occurs in common unit operations such as mills, grinders, dryers, and other modes of transport. The basic element for the setting of hazardous zone types consist of identifies release sources, determination of classification region of hazardous area, overviewing the basic operation in wheat flour processing plant with their specification requirement and use of a suitable code or calculations to determine area scope. Therefore, this analysis can be more elaborate by classifying the hazardous area into several areas using the International Electro Technical Commission System for Certification to Standards Relating to Equipment for Use in Explosive Atmospheres standard. Thus, wheat flour processing plant area classification can be categorized according to three zones based on the quantity of an explosion into atmosphere and its release frequencies which are zones 20, zones 21, and zones 22. From the results, it can be summarized that zone 20 is almost inside or closer one with the main equipment located near the ignition source which could lead to dust explosion, whereas zone 21 and zone 22 comes after zone 20 which is a less hazardous area as compared to zone 20 areas.

Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer counting, number of operations for matching similarity, memory utilization while performing similarity search, and processing time for stream matching. By suggesting an improved algorithm, Revised Rabin Karp(RRK) for basic operation and also to achieve more efficiency, the proposed solution suggests a novel framework based on Hadoop MapReduce blended with Pig & Apache Tez. The measure of memory utilization and processing time proposed model proves its efficiency when compared to existing approaches.

Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer counting, number of operations for matching similarity, memory utilization while performing similarity search, and processing time for stream matching. By suggesting an improved algorithm, Revised Rabin Karp(RRK) for basic operation and also to achieve more efficiency, the proposed solution suggests a novel framework based on Hadoop MapReduce blended with Pig & Apache Tez. The measure of memory utilization and processing time proposed model proves its efficiency when compared to existing approaches.

Smart node relocation (SNR) and connectivity restoration mechanism for wireless sensor networks

Node failures are inevitable in wireless sensor networks (WSNs) because sensor nodes in WSNs are miniature and equipped with small and often irreplaceable batteries. Due to battery drainage, sensor nodes can fail at any instance. Moreover, WSNs operate in hostile environments and environmental factors may also contribute to nodes failure. Failure of nodes leads to disruption of inter-node connectivity and might also lead to network partitioning. Failure to communicate with each other and with the base station can compromise the basic operation of the sensor network. For restoration of connectivity, a robust recovery mechanism is required. The existing connectivity restoration mechanisms suffer from shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity restoration. As a result, most of these mechanisms lead to the excessive mobility of nodes, which itself causes the utilization of excessive battery. In this work, we propose a novel technique called smart node relocation (SNR). SNR is capable of detecting and restoring the connectivity caused by either single or multiple node failures. For achieving energy efficiency, SNR relies on transmitting a lesser number of control packets. For achieving the goal of being coverage-aware, it tries to relocate only essential nodes while trying to restore connectivity. By performing extensive simulations, we prove that SNR outperforms the existing approaches concerning multiple performance metrics including but not limited to the total number of packets transmitted, total distance moved for connectivity restoration, the percentage reduction in field coverage.

Review of the shadow-test approach to adaptive testing

Constrained adaptive testing is reviewed as an instance of discrete maximization with the shadow-test approach delivering its solution. The approach may look counterintuitive in that it assumes sequential assembly of full test forms as its basic operation. But it always produces real-time solutions that are optimal and satisfy the set of specifications in effect for the test. Equally importantly, it can be used to run testing programs with different degrees of adaptation for the same set of specifications and/or as a tool to manage programs with simultaneous processes as adaptive item calibration, time management, and/or item-security monitoring.